Licensing of software that will run in offline environments introduces constraints on the licensing model that can be supported. The difficulty arises in cases where information needs to be recorded about the usage of the software, for example in a consumption-based licensing model. We describe a method that makes it harder for an adversary to tamper with the recorded data as well as ways for software vendors to detect if tampering with this information would still occur.

Encrypted databases systems and searchable encryption schemes still leak critical information (e.g.: access patterns) and require a choice between privacy and efficiency. We show that using ORAM schemes as a black-box is not a panacea and that optimizations are still possible by improving the data structures. We design an ORAM-based secure database that is built from the ground up: we replicate the typical data structure of a database system using different optimized ORAM constructions and derive a new solution for oblivious searches on databases. Our construction has a lower bandwidth overhead than state-of-the-art ORAM constructions by moving client-side computations to a proxy with an intermediate (rigorously defined) level of trust, instantiated as a server-side isolated execution environment. We formally prove the security of our construction and show that its access patterns depend only on public information. We also provide an implementation compatible with SQL databases (PostgresSQL). Our system is 1.2 times to 4 times faster than state-of-the-art ORAM-based solutions.

Ring signatures are a common construction used to provide signer ambiguity among a non-interactive set of public keys specified at the time of signing. Unlike early approaches where signature size is linear in the size of the signer anonymity set, current optimal solutions either require centralized trusted setups or produce signatures logarithmic in size. However, few also provide linkability, a property used to determine whether the signer of a message has signed any previous message, possibly with restrictions on the anonymity set choice. Here we introduce Triptych, a family of linkable ring signatures without trusted setup that is based on generalizations of zero-knowledge proofs of knowledge of commitment openings to zero. We demonstrate applications of Triptych in signer-ambiguous transaction protocols by extending the construction to openings of parallel commitments in independent anonymity sets. Signatures are logarithmic in the anonymity set size and, while verification complexity is linear, collections of proofs can be efficiently verified in batches. We show that for anonymity set sizes practical for use in distributed protocols, Triptych offers competitive performance with a straightforward construction.

In many machine learning applications, training data consists of sensitive information from multiple sources. Privacy-preserving machine learning using secure computation enables multiple parties to compute on their joint data without disclosing their inputs to each other. In this work, we focus on clustering, an unsupervised machine learning technique that partitions data into groups. Previous works on privacy-preserving clustering often leak information and focus on the k-means algorithm, which provides only limited clustering quality and flexibility. Additionally, the number of clusters k must be known in advance. We analyze several prominent clustering algorithms' capabilities and their compatibility with secure computation techniques to create an efficient, fully privacy-preserving clustering implementation superior to k-means. We find affinity propagation to be the most promising candidate and securely implement it using various multi-party computation techniques. Privacy-preserving affinity propagation does not require any input parameters and consists of operations hat are relatively efficient with secure computation. As threat models, we consider passive security as well as active security with an honest and dishonest majority. We offer the first comparison of privacy-preserving clustering between these scenarios, enabling an understanding of the exact trade-offs between them. Based on the clustering quality and the computational and communication costs, privacy-preserving affinity propagation offers a good trade-off between quality and efficiency for practical privacy-preserving clustering.

Businesses that are subject to AML/CTF regulation must meet their KYC obligations. In this context, to establish and verify a customer’s identity, the customer is required to share personal information with these businesses. This creates a Pareto dominated situation where a customer’s privacy is typically traded off for the mandated transparency requirements. In addition, this privacy erosion also reduces the security and safety of the customer as shared personal information can be passed on or stolen and used against the best interest of the customer (e.g. identity theft). Recent innovations in self-sovereign identity and zero-knowledge cryptography, along with proper ecosystem design, allow for a novel approach to KYC that protects the customer’s privacy without reducing transparency. The proposed solution concept, zkKYC, removes the need for the customer to share any personal information with a regulated business for the purpose of KYC, and yet provides the transparency to allow for a customer to be identified if and when that is ruled necessary by a designated governing entity (e.g. regulator, law enforcement). This approach breaks the traditional privacy vs. transparency trade-off and provides structured transparency, resulting in a net positive outcome for all parties involved.

Game-theoretic analyses of cryptocurrencies and---more generally---blockchain-based decentralized ledgers offer insight on their economic robustness and behavior when even their underpinning cryptographic assumptions fail. In this work we utilize the recently proposed blockchain adaptation of the rational protocol design (RPD) framework [EUROCRYPT '18] to analyze 51% double-spending attacks against Nakamoto-style proof-of-work based cryptocurrencies. We first observe a property of the originally proposed utility class that yields an unnatural conclusion against such attacks, and show how to devise a utility that avoids this pitfall and makes predictions that match the observable behavior---i.e., that renders attacking a dominant strategy in settings where an attack was indeed observed in reality. We then propose a generic remedy to the underlying protocol parameters that provably deter adversaries controlling a majority of the system's resources from attacks on blockchain consistency, including the 51% double-spending attack. This can be used as guidance to patch systems that have suffered such attacks, e.g., Ethereum Classic and Bitcoin Cash, and serves as a demonstration of the power of game-theoretic analyses.

In recent years, research has shown the networking layer’s significant influence on the scalability, security, and privacy of blockchain systems. Such large-scale networks however exhibit a degree of complexity that demands model-based simulations as real-world experiments are often not possible. In this work, we methodically characterize blockchain networks by reference to the paradigmatic Bitcoin peer-to-peer network, explore the state-of-the-art protocols, and emphasize this key design space. To this end, we conducted a longitudinal measurement study on the Bitcoin network, from which we extract a comprehensive network model and implement it as part of the bns network simulation framework. We validate the model in comparison to real-world measurements as well as to results from related work. Moreover, we experimentally show how network utilization and miners’ geographical location impact the block propagation characteristics.

While privacy preserving distributed payment schemes manage to drastically improve user privacy, they come at the cost of generating new regulatory concerns: in a private ledger the transactions cannot be subject to any level of auditing, and thus are not compatible with tracing illegal behaviors. In this work we present MiniLedger, a distributed payment system which not only guarantees the privacy of transactions, but also offers built-in functionalities for various types of audits by any external authority. MiniLedger is the first private and auditable payment system with storage costs independent of the number of transactions. To achieve such a storage improvement, we introduce pruning functionalities for the transaction history while maintaining integrity and auditing. We provide formal security definitions and a number of extensions for various auditing levels. Our evaluation results show that MiniLedger is practical in terms of storage requiring as low as 70KB per participant for 128 bits of security, and depending on the implementation choices, can prune 1 million transactions in less than a second.

This is team SmartPools’ submission for the first Ergo Hackathon. It suggests that Ergo lacks the decentralization, and focus on regular people that it was designed for, and presents a potential solution for these problems in laying the framework for crowdfunded smart contract pools compatible with non-outsourceabilty. It should allow for pool formation with a greater level of decentralization than previously possible by including metrics for diminishing returns on over-contributing hash power to pools with data gathered from Ergo Oracles. This work is informal and preliminary. Further research is required to formalize this work and attempt to provide functional proof for its arguments; readers are highly encouraged to read the included references, and their references, for greater clarity.

Cellular networks connect nearly every human on the planet; they consequently have visibility into location data and voice, SMS, and data contacts and communications. Such near-universal visibility represents a significant threat to the privacy of mobile subscribers. In 5G networks, end-user mobile device manufacturers assign a Permanent Equipment Identifier (PEI) to every new device. Mobile operators legitimately use the PEI to blocklist stolen devices from the network to discourage device theft, but the static PEI also provides a mechanism to uniquely identify and track subscribers. Advertisers and data brokers have also historically abused the PEI for data fusion of location and analytics data, including private data sold by cellular providers. In this paper, we present a protocol that allows mobile devices to prove that they are not in the blocklist without revealing their PEI to any entity on the network. Thus, we maintain the primary purpose of the PEI while preventing potential privacy violations. We describe provably secure anonymous proof of blocklist non-membership for cellular network, based on the RSA accumulators and zero-knowledge proofs introduced by Camenisch and Lysyanskaya (Crypto'02) and expanded upon by Li, Li and Xue (ACNS'07). We show experimentally that this approach is viable for cellular networks: a phone can create a blocklist non-membership proof in only 3432 milliseconds of online computation, and the network can verify the proof in less than one second on average. In total this adds fewer than 4.5 seconds to the rare network attach process. This work shows that PEIs can be attested anonymously in 5G and future network generations, and it paves the way for additional advances toward a cellular network with guaranteed privacy.

Ridesharing is revolutionizing the transportation industry in many countries. Yet, the state of the art is based on heavily centralized services and platforms, where the service providers have full possession of the users’ location data. Recently, researchers have started addressing the challenge of enabling privacy-preserving ridesharing. The initial proposals, however, have shortcomings, as some rely on a central party, some incur high performance penalties, and most do not consider time preferences for ridesharing. TOPPool encompasses ridesharing based on the proximity of end-points of a ride as well as partial itinerary overlaps. To achieve the latter, we propose a simple yet powerful reduction to a private set intersection on trips represented as sets of consecutive road segments. We show that TOPPool includes time preferences while preserving privacy and without relying on a third party. We evaluate our approach on real-world data from the New York’s Taxi & Limousine Commission. Our experiments demonstrate that TOPPool is superior in performance over the prior work: our intersection-based itinerary matching runs in less than 0.3 seconds for reasonable trip length, in contrast, on the same set of trips prior work takes up to 10 hours.

Clustering is a popular unsupervised machine learning technique that groups similar input elements into clusters. It is used in many areas ranging from business analysis to health care. In many of these applications, sensitive information is clustered that should not be leaked. Moreover, nowadays it is often required to combine data from multiple sources to increase the quality of the analysis as well as to outsource complex computation to powerful cloud servers. This calls for efficient privacy-preserving clustering. In this work, we systematically analyze the state-of-the-art in privacy-preserving clustering. We implement and benchmark today's four most efficient fully private clustering protocols by Cheon et al. (SAC'19), Meng et al. (ArXiv'19), Mohassel et al. (PETS'20), and Bozdemir et al. (ASIACCS'21) with respect to communication, computation, and clustering quality. We compare them, assess their limitations for a practical use in real-world applications, and conclude with open challenges.

The amount of encrypted Internet traffic almost doubles every year thanks to the wide adoption of end-to-end traffic encryption solutions such as IPSec, TLS and SSH. Despite all the benefits of user privacy the end-to-end encryption provides, the encrypted internet traffic blinds intrusion detection system (IDS) and makes detecting malicious traffic hugely difficult. The resulting conflict between the user's privacy and security has demanded solutions for deep packet inspection (DPI) over encrypted traffic. The approach of those solutions proposed to date is still restricted in that they require intensive computations during connection setup or detection. For example, BlindBox, introduced by Sherry et al. (SIGCOMM 2015) enables inspection over the TLS-encrypted traffic without compromising users' privacy, but its usage is limited due to a significant delay on establishing an inspected channel. PrivDPI, proposed more recently by Ning et al. (ACM CCS 2019), improves the overall efficiency of BlindBox and makes the inspection scenario more viable.Despite the improvement, we show in this paper that the user privacy of Ning et al.'s PrivDPI can be compromised entirely by the rule generator without involving any other parties, including the middlebox. Having observed the difficulties of realizing efficiency and security in the previous work, we propose a new DPI system for encrypted traffic, named ``Practical and Privacy-Preserving Deep Packet Inspection (P2DPI)''. P2DPI enjoys the same level of security and privacy that BlindBox provides. At the same time, P2DPI offers fast setup and encryption and outperforms PrivDPI. Our results are supported by formal security analysis. We implemented our P2DPI and comparable PrivDPI and performed extensive experimentation for performance analysis and comparison.

This paper studies the challenges of creating a mobile device based voting client. We discuss the issues related to standalone and mobile browser based voting applications. In both cases we discuss the problems of vote privacy, integrity and voting channel availability. We conclude that neither of the options can currently achieve the level of security PC-based voting clients can provide, with the attack surface being larger in the case of mobile browser based voting application.

We propose a smart contract that allows two mutually distrusting parties to transact any non-digital good or service on a blockchain. The contract acts as an escrow and settles disputes by letting parties wager that they can convince an arbiter they were the honest party. We analyze the contract as an extensive-form game and prove that the contract is secure in a strong game-theoretic sense if and only if the arbiter is biased in favor of honest parties. We show this is inherent to any contract that achieves game-theoretic security for interesting trades. We consider a generalization of the contract with different ways of paying back the wagers, and we can instantiate it to make a tradeoff between security and the size of the wager. By relaxing the security notion such that parties have only weak incentive to behave honestly, we can replace the arbiter by a random coin toss protocol. We implement the contract in Ethereum and estimate the amortized cost of running the contract as 2-3 USD for the seller and 4-5 USD for the buyer.

Over the last few decades, the cost and difficulty of producing integrated circuits at ever shrinking node sizes has vastly increased, resulting in the manufacturing sector moving overseas. Using offshore foundries for chip fabrication, however, introduces new vulnerabilities into the design flow since there is little to no observability into the manufacturing process. At the same time, both design and optimization are becoming increasingly complex, particularly as SoC designs gain popularity. Common practices such as porting a design across node sizes and reusing cores at multiple area/performance tradeoffs further complicate assurance as layout specific features impede comparison. Methods have been developed for conducting integrated circuit decomposition on fabricated chips [1][2][16] to extract the as-fabricated design files such as the GDSII layout or gate-level netlist. While mature netlist equivalency checking tools are included with any design flow, there is a lack of tools for performing deeper analyses on the extracted designs for the purposes of hardware assurance or design recovery from obsolete parts. To this end, there is a need for a tool to extract functionality from netlists at a higher abstraction level to reconstruct behavioral Register Transfer Level (RTL) code.

The proliferation of the Internet of Things (IoT) technology has made ubiquitous computing a reality by broadening Internet connectivity across diverse application domains, thus bridging billions of devices and human beings as well for information collection, data processing, and decision-making. In recent years, IoT technology and its applications in various industrial sectors have grown exponentially. Most existing industrial IoT (IIoT) implementations, however, are still relying on a centralized architecture, which is vulnerable to the single point of failure attack and requires a massive amount of computation at the central entity. The emerging blockchain technology is currently undergoing rapid development and has the full potential to revolutionize the IIoT platforms and applications. As a distributed and decentralized tamper-resistant ledger, blockchain maintains the consistency of data records at different locations and holds the potential to address the issues in traditional IIoT networks, such as heterogeneity, interoperability, and security. Integrating the blockchain technology into IIoT platforms requires to address several critical challenges that are inherent in IIoT and blockchain themselves, such as standardization, scalability, and interoperability. This paper provides a comprehensive review on the recent advances in architecture design and technology development towards tackling these challenges. We further provide several representative industrial use cases that can benefit from the integration of blockchain technology, and discuss the recent research trends and open issues in blockchain-enabled IIoT platforms.

Secure aggregation is a critical component in federated learning, which enables the server to learn the aggregate model of the users without observing their local models. Conventionally, secure aggregation algorithms focus only on ensuring the privacy of individual users in a single training round. We contend that such designs can lead to significant privacy leakages over multiple training rounds, due to partial user selection/participation at each round of federated learning. In fact, we empirically show that the conventional random user selection strategies for federated learning lead to leaking users' individual models within number of rounds linear in the number of users. To address this challenge, we introduce a secure aggregation framework with multi-round privacy guarantees. In particular, we introduce a new metric to quantify the privacy guarantees of federated learning over multiple training rounds, and develop a structured user selection strategy that guarantees the long-term privacy of each user (over any number of training rounds). Our framework also carefully accounts for the fairness and the average number of participating users at each round. We perform several experiments on MNIST and CIFAR-10 datasets in the IID and the non-IID settings to demonstrate the performance improvement over the baseline algorithms, both in terms of privacy protection and test accuracy.

Many organizations need large amounts of high-quality data for their applications, and one way to acquire such data is to combine datasets from multiple parties. Since these organizations often own sensitive data that cannot be shared in the clear with others due to policy regulation and business competition, there is increased interest in utilizing secure multi-party computation (MPC). MPC allows multiple parties to jointly compute a function without revealing their inputs to each other. We present Cerebro, an end-to-end collaborative learning platform that enables parties to compute learning tasks without sharing plaintext data. By taking an end-to-end approach to the system design, Cerebro allows multiple parties with complex economic relationships to safely collaborate on machine learning computation through the use of release policies and auditing, while also enabling users to achieve good performance without manually navigating the complex performance tradeoffs between MPC protocols.

In recent years machine learning has become increasingly mainstream across industries. Additionally, Graphical Processing Unit (GPU) accelerators are widely deployed in various neural network (NN) applications, including image recognition for autonomous vehicles and natural language processing, among others. Since training a powerful network requires expensive data collection and computing power, its design and parameters are often considered a secret intellectual property of their manufacturers. However, hardware accelerators can leak crucial information about the secret neural network designs through side-channels, like Electro-Magnetic (EM) emanations, power consumption, or timing. We propose and evaluate non-invasive and passive reverse engineering methods to recover NN designs deployed on GPUs through EM side-channel analysis. We employ a well-known technique of simple EM analysis and timing analysis of NN layers execution. We consider commonly used NN architectures, namely Multilayer Perceptron and Convolutional Neural Networks. We show how to recover the number of layers and neurons as well as the types of activation functions. Our experimental results are obtained on a setup that is as close as possible to a real-world device in order to properly assess the applicability and extendability of our methods. We analyze the NN execution of a PyTorch python framework implementation running on Nvidia Jetson Nano, a module computer embedding a Tegra X1 SoC that combines an ARM Cortex-A57 CPU and a 128-core GPU within a Maxwell architecture. Our results show the importance of side-channel protections for NN accelerators in real-world applications.

In this technical report we describe E3 (Encrypt-Everything-Everywhere), a framework which enables execution of standard C++ code with homomorphically encrypted variables. The framework automatically generates protected types so the programmer can remain oblivious to the underlying encryption scheme. C++ protected classes redefine operators according to the encryption scheme effectively making the introduction of a new API unnecessary. At its current version, E3 supports a variety of homomorphic encryption libraries, batching, mixing different encryption schemes in the same program, as well as the ability to combine modular computation and bit-level computation.

IBM Digital Health Pass (IDHP) is a technology developed by IBM offering the technical infrastructure to allow individuals to prove their COVID19-related health status (e.g., whether that individual was tested negative for COVID19, has been partially/fully vaccinated, or recovered from COVID19) to third parties in a secure and privacy-respectful way. In a nutshell, IBM Digital Health Pass technology enables issuers, i.e., authorised healthcare providers onboarded to the system by health authorities of a given country or jurisdiction, to produce digital attestations about individuals’ health status. These attestations, called Health Certificates are issued to individuals, called subjects or holders, and are stored on a piece of paper or within subjects’ mobile phone wallets. Subjects can then demonstrate the authenticity of one or more of their Health Certificates to third parties of their choice called verifiers, when the necessity of demonstrating COVID19 related health status arises. Subjects can also demonstrate their association with each of their Health Certificates. IBM Digital Health Pass is built around preserving individuals’ privacy as a first-class requirement, based on established public key cryptography concepts in a way that can easily scale to millions of Health Certificates.

Today’s society depends on interconnected electronic devices, which handle various sensitive information. Due to the knowledge needed to develop these devices and the economic advantage of reusable solutions, most of these systems contain Third-Party Intellectual Property (3PIP) cores that might not be trustworthy. If one of these 3PIP cores is vulnerable, the security of the entire device is potentially affected. As a result, sensitive data that is processed by the device can be leaked to an attacker. Competitions like Hack@DAC serve as a playground to develop and examine novel approaches and computer-aided tools that identify security vulnerabilities in System-on-Chip (SoC) Register-Transfer-Level (RTL) designs. In this paper, we present a successful divide and conquer approach to test SoC security which is illustrated by exemplary RTL vulnerabilities in the competition’s SoC design. Additionally, we craft real-world software attacks that exploit these vulnerabilities.

Despite a long history of research and wide-spread applications to censorship resistant systems, practical steganographic systems capable of embedding messages into realistic communication distributions, like text, do not exist. We identify two primary impediments to deploying universal steganography: (1) prior work leaves the difficult problem of finding samplers for non-trivial distributions unaddressed, and (2) prior constructions have impractical minimum entropy requirements. We investigate using generative models as steganographic samplers, as they represent the best known technique for approximating human communication. Additionally, we study methods to overcome the entropy requirement, including evaluating existing techniques and designing a new steganographic protocol, called Meteor. The resulting protocols are provably indistinguishable from honest model output and represent an important step towards practical steganographic communication for mundane communication channels. We implement Meteor and evaluate it on multiple computation environments with multiple generative models.

Secure deduplication allows removing duplicate content at third-party storage services while preserving the privacy of users’ data. However, current solutions are built with strict designs that cannot be adapted to storage service and applications with different security and performance requirements. We present S2Dedup, a trusted hardware-based privacy-preserving deduplication system designed to support multiple security schemes that enable different levels of performance, security guarantees and space savings. An in-depth evaluation shows these trade-offs for the distinct Intel SGX-based secure schemes supported by our prototype. Moreover, we propose a novel Epoch and Exact Frequency scheme that prevents frequency analysis leakage attacks present in current deterministic approaches for secure deduplication while maintaining similar performance and space savings to state-of-the-art approaches.

Metadata from voice calls, such as the knowledge of who is communicating with whom, contains rich information about people’s lives. Indeed, it is a prime target for powerful adversaries such as nation states. Existing systems that hide voice call metadata either require trusted intermediaries in the network or scale to only tens of users. This paper describes the design, implementation, and evaluation of Addra, the first system for voice communication that hides metadata over fully untrusted infrastructure and scales to tens of thousands of users. At a high level, Addra follows a template in which callers and callees deposit and retrieve messages from private mailboxes hosted at an untrusted server. However, Addra improves message latency in this architecture, which is a key performance metric for voice calls. First, it enables a caller to push a message to a callee in two hops, using a new way of assigning mailboxes to users that resembles how a post office assigns PO boxes to its customers. Second, it innovates on the underlying cryptographic machinery and constructs a new private information retrieval scheme, FastPIR, that reduces the time to process oblivious access requests for mailboxes. An evaluation of Addra on a cluster of 80 machines on AWS demonstrates that it can serve 32K users with a 99-th percentile message latency of 726 ms—a 7× improvement over a prior system for text messaging in the same threat model.

FIDO’s U2F is a web-authentication mechanism designed to mitigate real-time phishing—an attack that undermines multi-factor authentication by allowing an attacker to relay second-factor one-time tokens from the victim user to the legitimate website in real-time. A U2F dongle is simple to use, and is designed to restrain users from using it incorrectly. We show that social engineering attacks allow an adversary to downgrade FIDO’s U2F to alternative authentication mechanisms. Websites allow such alternatives to handle dongle malfunction or loss. All FIDO-supporting websites in Alexa’s top 100 allow choosing alternatives to FIDO, and are thus potentially vulnerable to real-time phishing attacks. We crafted a phishing website that mimics Google login’s page and implements a FIDO-downgrade attack. We then ran a carefully-designed user study to test the effect on users. We found that, when using FIDO as their second authentication factor, 55% of participants fell for real-time phishing, and another 35% would potentially be susceptible to the attack in practice.

We present CanDID, a platform for practical, user-friendly realization of decentralized identity, the idea of empowering end users with management of their own credentials. While decentralized identity promises to give users greater control over their private data, it burdens users with management of private keys, creating a significant risk of key loss. Existing and proposed approaches also presume the spontaneous availability of a credential-issuance ecosystem, creating a bootstrapping problem. They also omit essential functionality, like resistance to Sybil attacks and the ability to detect misbehaving or sanctioned users while preserving user privacy. CanDID addresses these challenges by issuing credentials in a user-friendly way that draws securely and privately on data from existing, unmodified web service providers. Such legacy compatibility similarly enables CanDID users to leverage their existing online accounts for recovery of lost keys. Using a decentralized committee of nodes, CanDID provides strong confidentiality for user's keys, real-world identities, and data, yet prevents users from spawning multiple identities and allows identification (and blacklisting) of sanctioned users. We present the CanDID architecture and its technical innovations and report on experiments demonstrating its practical performance.

Cloud storage services are top-rated, but there are often concerns about the security of the files there stored. Clouds-of-clouds or multi-clouds are being explored in order to improve that security. The idea is to store the files in several clouds, ensuring integrity and availability. Confidentiality, however, is obtained by encrypting the files with block ciphers that do not provide provable security. Secret sharing allows distributing files among the clouds providing information-theoretic security/secrecy. However, existing secret sharing schemes are space-inefficient (the size of the shares is much larger than the size of the secret) or purely theoretical. In this paper, we propose the first practical space-efficient secret sharing scheme that provides information-theoretic security, which we denominate PRactical Efficient Secret Sharing (PRESS). Moreover, we present the Secure CloUD storage (SCUD) service, a new cloud-of-clouds storage service that leverages PRESS to provide file confidentiality. Additionally, SCUD provides data integrity and availability, leveraging replication.

Today's microprocessors often rely on microcode updates to address issues such as security or functional patches. Unfortunately, microcode update flexibility opens up new attack vectors through malicious microcode alterations. Such attacks share many features with hardware Trojans and have similar devastating consequences for system security. However, due to microcode's opaque nature, little is known in the open literature about the capabilities and limitations of microcode Trojans. We introduce the design of a microcoded RISC-V processor architecture together with a microcode development and evaluation environment. Even though microcode typically has almost complete control of the processor hardware, the design of meaningful microcode Trojans is not straightforward. This somewhat counter-intuitive insight is due to the lack of information at the hardware level about the semantics of executed software. In three security case studies we demonstrate how to overcome these issues and give insights on how to design meaningful microcode Trojans that undermine system security. To foster future research and applications, we publicly release our implementation and evaluation platform.

Mesh messaging applications allow users in relative proximity to communicate without the Internet. The most viable offering in this space, Bridgefy, has recently seen increased uptake in areas experiencing large-scale protests (Hong Kong, India, Iran, US, Zimbabwe, Belarus), suggesting its use in these protests. It is also being promoted as a communication tool for use in such situations by its developers and others. In this work, we report on a security analysis of Bridgefy. Our results show that Bridgefy, as analysed, permitted its users to be tracked, offered no authenticity, no effective confidentiality protections and lacked resilience against adversarially crafted messages. We verified these vulnerabilities by demonstrating a series of practical attacks on Bridgefy. Thus, if protesters relied on Bridgefy, an adversary could produce social graphs about them, read their messages, impersonate anyone to anyone and shut down the entire network with a single maliciously crafted message.

Reverse engineering of integrated circuits, i.e., understanding the internals of IC, is required for many benign and malicious applications. Examples of the former are detection of patent infringements, hardware Trojans or IP-theft, as well as interface recovery and defect analysis, while malicious applications include IP-theft and finding insertion points for hardware Trojans. However, regardless of the application, the reverse engineer initially starts with a large unstructured netlist, forming an incomprehensible sea of gates. This work presents DANA, a generic, technology-agnostic, and fully automated dataflow analysis methodology for flattened gate-level netlists. By analyzing the flow of data between individual FF, DANA recovers high-level registers. The key idea behind DANA is to combine independent metrics based on structural and control information with a powerful automated architecture. Notably, DANA works without any thresholds, scenario-dependent parameters, or other "magic" values that the user must choose. We evaluate DANA on nine modern hardware designs, ranging from cryptographic co-processors, over CPUs, to the OpenTitan, a state-of-the-art SOC, which is maintained by the lowRISC initiative with supporting industry partners like Google and Western Digital. Our results demonstrate almost perfect recovery of registers for all case studies, regardless whether they were synthesized as FPGA or ASIC netlists. Furthermore, we explore two applications for dataflow analysis: we show that the raw output of DANA often already allows to identify crucial components and high-level architecture features and also demonstrate its applicability for detecting simple hardware Trojans. Hence, DANA can be applied universally as the first step when investigating unknown netlists and provides major guidance for human analysts by structuring and condensing the otherwise incomprehensible sea of gates. Our implementation of DANA and all synthesized netlists are available as open source on GitHub.

Access and search pattern leakage from range queries are detrimental to the security of encrypted databases, as evidenced by a large body of work on efficient attacks that reconstruct one-dimensional databases. Recently, the first attack from 2D range queries showed that higher-dimensional databases are also in danger. This attack requires complete information for reconstruction. In this paper, we develop reconstructions that require less information. We present an order reconstruction attack that only depends on access pattern leakage, and empirically show that the order allows the attacker to infer the geometry of the underlying data. Notably, this attack also achieves full database reconstruction when the 1D horizontal and vertical projections of the points are dense. We also give an approximate database reconstruction attack that is distribution-agnostic and works with any subset of the possible search pattern, given the order of the database. Finally, we show how knowledge of auxiliary information such as the centroid of a related dataset allows to improve the reconstruction. We support our results with formal analysis and experiments on real-world databases and queries drawn from various distributions.

Using passwords for authentication has been proven vulnerable in countless security incidents. Hardware authentication tokens effectively prevent most password-related security issues and improve security indisputably. However, we would like to highlight that there are new threats from attackers with physical access which need to be discussed. Supply chain adversaries may manipulate devices on a large scale and install backdoors before they even reach end users. In evil maid scenarios, specific devices may even be attacked while already in use. Hence, we thoroughly investigate the security and trustworthiness of eight commercially available open source authentication tokens, including devices from the two market leaders: SoloKeys and Nitrokey. Unfortunately, we identify and practically verify significant vulnerabilities in all eight examined tokens. Some of them based on severe, previously undiscovered, vulnerabilities of three major microcontroller products which are used at a large scale in various products. Our findings clearly emphasize the significant threat from supply chain and evil maid scenarios since the attacks are practical and only require moderate attacker efforts. Fortunately, we are able to describe software-based countermeasures as effective improvements to retrofit the examined devices. To improve the security and trustworthiness of future authentication tokens, we also derive important general design recommendations.

In this paper, we consider the problem of cross-chain transactions where parties that do not trust each other safely exchange digital assets across blockchains. Open blockchains models are decentralized ledgers that keep records of transactions. They are comparable with distributed account books. While they have proven their potential as a store of value, exchanging assets across several blockchains remains a challenge. Our paper proposes a new protocol, R-SWAP, for cross-chain swaps that outperforms existing solutions. Our protocol is built on top of two abstractions: relays and adapters that we formalize for the first time in this paper. Furthermore, we prove the correctness of R-SWAP and analytically evaluate its performances, in terms of cost and latency. Moreover, we evaluate the performances of R-SWAP in two case studies showing the generality of our approach: atomic swaps between Ethereum and Bitcoin (two popular permissionless blockchains) and atomic swaps between Ethereum and Tendermint (one permissionless and one permissioned blockchain).

Clustering is an unsupervised machine learning technique that outputs clusters containing similar data items. In this work, we investigate privacy-preserving density-based clustering which is, for example, used in financial analytics and medical diagnosis. When (multiple) data owners collaborate or outsource the computation, privacy concerns arise. To address this problem, we design, implement, and evaluate the first practical and fully private density-based clustering scheme based on secure two-party computation. Our protocol privately executes the DBSCAN algorithm without disclosing any information (including the number and size of clusters). It can be used for private clustering between two parties as well as for private outsourcing of an arbitrary number of data owners to two non-colluding servers. Our implementation of the DBSCAN algorithm privately clusters data sets with 400 elements in 7 minutes on commodity hardware. Thereby, it flexibly determines the number of required clusters and is insensitive to outliers, while being only factor 19x slower than today's fastest private K-means protocol (Mohassel et al., PETS'20) which can only be used for specific data sets. We then show how to transfer our newly designed protocol to related clustering algorithms by introducing a private approximation of the TRACLUS algorithm for trajectory clustering which has interesting real-world applications like financial time series forecasts and the investigation of the spread of a disease like COVID-19.

We present ZEN, the first optimizing compiler that generates efficient verifiable, zero-knowledge neural network inference schemes. ZEN generates two schemes: ZEN$_{acc}$ and ZEN$_{infer}$. ZEN$_{acc}$ proves the accuracy of a committed neural network model; ZEN$_{infer}$ proves a specific inference result. Used in combination, these verifiable computation schemes ensure both the privacy of the sensitive user data as well as the confidentiality of the neural network models. However, directly using these schemes on zkSNARKs requires prohibitive computational cost. As an optimizing compiler, ZEN introduces two kinds of optimizations to address this issue: first, ZEN incorporates a new neural network quantization algorithm that incorporate two R1CS friendly optimizations which makes the model to be express in zkSNARKs with less constraints and minimal accuracy loss; second, ZEN introduces a SIMD style optimization, namely stranded encoding, that can encoding multiple 8bit integers in large finite field elements without overwhelming extraction cost. Combining these optimizations, ZEN produces verifiable neural network inference schemes with ${\bf 5.43} \sim {\bf 22.19} \times$ ($15.35 \times$ on average) less R1CS constraints.

Digital contact tracing apps allow to alert people who have been in contact with people who may be contagious. The Google/Apple Exposure Notification (GAEN) system is based on Bluetooth proximity estimation. It has been adopted by many countries around the world. However, many possible attacks are known. The goal of some of them is to inject a false alert on someone else's phone. This way, an adversary can eliminate a competitor in a sport event or a business in general. Political parties can also prevent people from voting. In this report, we review several methods to inject false alerts. One of them requires to corrupt the clock of the smartphone of the victim. For that, we build a time-traveling machine to be able to remotely set up the clock on a smartphone and experiment our attack. We show how easy this can be done. We successfully tested several smartphones with either the Swiss or the Italian app (SwissCovid or Immuni). We confirms is also works on other GAEN-based apps: NHS COVID-19 (in England and Wales), Corona-Warn-App (in Germany), and Coronalert (Belgium). The time-machine can also be used in active attack to identify smartphones. We can recognize smartphones that we have passively seen in the past. We can passively recognize in the future smartphones that we can see in present. We can also make smartphones identify themselves with a unique number. Finally, we report a simpler attack which needs no time machine but relies on the existence of still-valid keys reported on the server. We observed the case in several countries. The attack is made trivial in Austria, Denmark, Spain, Italy, the Netherlands, Alabama, Delaware, Wyoming, Canada, and England & Whales. Other regions are affected by interoperability too.

Several emerging proof-of-work (PoW) blockchain protocols rely on a “parallel-chain” architecture for scaling, where instead of a single chain, multiple chains are run in parallel and aggregated. A key requirement of practical PoW blockchains is to adapt to mining power variations over time (Bitcoin’s total mining power has increased by a $10^{14}$ factor over the decade). In this paper, we consider the design of provably secure parallel-chain protocols which can adapt to such mining power variations. The Bitcoin difficulty adjustment rule adjusts the difficulty target of block mining periodically to get a constant mean inter-block time. While superficially simple, the rule has proved itself to be sophisticated and successfully secure, both in practice and in theory [11, 13]. We show that natural adaptations of the Bitcoin adjustment rule to the parallel-chain case open the door to subtle, but catastrophic safety and liveness breaches. We uncover a meta-design principle that allow us to design variable mining difficulty protocols for three popular PoW blockchain proposals (Prism [3], OHIE [26], Fruitchains [21]) inside a common rubric. The principle has three components: (M1) a pivot chain, based on which blocks in all chains choose difficulty, (M2) a monotonicity condition for referencing pivot chain blocks and (M3) translating additional protocol aspects from using levels (depth) to using “difficulty levels”. We show that protocols employing a subset of these principles may have catastrophic failures. The security of the designs is also proved using a common rubric – the key technical challenge involves analyzing the interaction between the pivot chain and the other chains, as well as bounding the sudden changes in difficulty target experienced in non-pivot chains. We empirically investigate the responsivity of the new mining difficulty rule via simulations based on historical Bitcoin data, and find that the protocol very effectively controls the forking rate across all the chains.

A great challenge for distributed payment systems is their compliance with regulations, such as anti-money laundering, insolvency legislation, countering the financing of terrorism and sanctions laws. After Bitcoin's MtGox scandal, one of the most needed auditing functionalities for financial solvency and tax reporting purposes is to prove ownership of blockchain reserves, a process known as Proof of Assets (PoA). This work formalizes the PoA requirements in account-based blockchains, focusing on the unique hierarchical account structure of the Diem blockchain, formerly known as Libra. In particular, we take into account some unique features of the Diem infrastructure to consider different PoA modes by exploring time-stamping edge cases, cold wallets, locked assets, spending ability delegation and account pruning, among the others. We also propose practical optimizations to the byte-size of PoA in the presence of light clients who cannot run a full node, including skipping Validator updates, while still maintaining the 66.7% Byzantine fault tolerance (BFT) guarantee.

In 2019, the ANSSI released a protected software implementation of AES running on an STM32 platform with ARM Cortex-M architecture, publicly available on Github. The release of the code was shortly followed by a first paper written by Bronchain et al. at Ches 2020, analyzing the security of the implementation and proposing some attacks. In order to propose fair comparisons for future attacks on this target device, this paper aims at presenting a new publicly available dataset, called ASCADv2 based on this implementation. Along with the dataset, we also provide a benchmark of deep learning based side-channel attacks, thereby extending the works of Bronchain et al. Our attacks revisit and leverage the multi-task learning approach, introduced by Maghrebi in 2020, in order to efficiently target several intermediate computations at the same time. We hope that this work will draw the community’s interest towards the evaluation of highly protected software AES, whereas some of the current public SCA datasets are nowadays reputed to be less and less challenging.

Macroeconomic policy in a blockchain system concerns the algorithm that decides the payment schedule for miners and thus its money mint rate. It governs the amounts, distributions, beneficiaries and conditions required for money supply payments to participants by the system. While most chains today employ simple policies such as a constant amount per block, several cryptocurrencies have sprung up that put forth more interesting policies. As blockchains become a more popular form of money, these policies inevitably are becoming more complex. A chain with a simple policy will often need to switch over to a different policy. Until now, it was believed that such upgrades require a hard fork -- after all, they are changing the money supply, a central part of the system, and unupgraded miners cannot validate blocks that deviate from those hard-coded rules. In this paper, we present a mechanism that allows a chain to upgrade from one policy to another through a soft fork. Our proposed mechanism works in today's Ethereum blockchain without any changes and can support a very generic class of monetary policies that satisfy a few basic bounds. Our construction is presented in the form of a smart contract. We showcase the usefulness of our proposal by describing several interesting applications of policy changes. Notably, we put forth a mechanism that makes Non-Interactive Proofs of Proof-of-Work unbribable, a previously open problem.

Proof of Work (PoW) blockchains regulate the frequency and security of extensions to the blockchain in a decentralized manner by adjusting the difficulty in the network. However, analogous decentralized measures to regulate the replication level of the associated transactions and blocks data are completely missing so far. We argue that such measures are required as well. On the one hand, the smaller the number of replicas, the higher the vulnerability of the system against compromises and DoS-attacks. On the other hand, the larger the number of replicas, the higher the storage overhead, and the higher the operational blockchain cost are. In this paper, we propose a novel solution, EWoK (Entangled proofs of WOrk and Knowledge), that regulates in a decentralized manner the minimum number of replicas that should be stored by miners in the blockchain. EWoK achieves this by tying replication to the only directly-incentivized process in PoW-blockchains – which is PoW itself. EWoK only incurs small modifications to existing PoW protocols and is fully compliant with the specifications of existing mining hardware. Our implementation results confirm that EWoK can be easily integrated within existing mining pool protocols, such as GetBlockTemplate and Stratum mining, and does not impair the mining efficiency.

Many studies focus on the blockchain privacy protection. Unfortunately, the privacy protection brings some issues (e.g., money-laundering problem). Tracing users' identities is a critical step in addressing these issues. When each user's identity in the blockchain data is determined, the regulator can do some regulatory operations (such as Big Data analysis) to decide who should be punished or who should own the lost data. In this paper, we propose SkyEye, a traceable scheme for blockchain, that can be applied to a class of blockchain application. SkyEye enables the regulator to trace users' identities. Moreover, we demonstrate the security of SkyEye under specific cryptographic assumptions. Finally, we implement two prototypes of SkyEye, and evaluate the running time and related data storage requirements by performing the aforementioned prototypes.

During the last years, many Physical Zero-knowledge Proof(ZKP) protocols for Nikoli’s puzzles have been designed. In this paper, we propose two ZKP protocols for the two Nikoli’s puzzles called Nurikabe and Hitori. These two puzzles have some similarities, since in their rules at least one condition requires that some cells are connected to each other, horizontally or vertically. The novelty in this paper is to propose two techniques that allow us to prove such connectivity without leaking any information about a solution.

As cloud computing becomes more popular, research has focused on usable solutions to the problem of verifiable computation (VC), where a computationally weak device (Verifier) outsources a program execution to a powerful server (Prover) and receives guarantees that the execution was performed faithfully. A Prover can further demonstrate knowledge of a secret input that causes the Verifier’s program to satisfy certain assertions, without ever revealing which input was used. State-of-the-art Zero-Knowledge Proofs of Knowledge (ZKPK) methods encode a computation using arithmetic circuits and preserve the privacy of Prover’s inputs while attesting the integrity of program execution. Nevertheless, developing, debugging and optimizing programs as circuits remains a daunting task, as most users are unfamiliar with this programming paradigm. In this work we present Zilch, a framework that accelerates and simplifies the deployment of VC and ZKPK for any application transparently, i.e., without the need of trusted setup. Zilch uses traditional instruction sequences rather than static arithmetic circuits that would need to be regenerated for each different computation. Towards that end we have implemented ZMIPS: a MIPS-like processor model that allows verifying each instruction independently and compose a proof for the execution of the target application. To foster usability, Zilch incorporates a novel cross-compiler from an object-oriented Java-like language tailored to ZKPK and optimized our ZMIPS model, as well as a powerful API that enables integration of ZKPK within existing C/C++ programs. In our experiments, we demonstrate the flexibility of Zilch using two real-life applications, and evaluate Prover and Verifier performance on a variety of benchmarks.

Distributed ledger technologies like blockchain have gained great attention in both academia and industry. Blockchain as a potentially disruptive technology can advance many different fields, e.g., cryptocurrencies, supply chains, and the industrial Internet of Things. The next-generation blockchain ecosystem is expected to consist of various homogeneous and heterogeneous distributed ledgers. These ledger systems will inevitably require a certain level of proper cooperation of multiple blockchains to enrich advanced functionalities and enhance interoperable capabilities for future applications. The interoperability among blockchains will definitely revolutionize current blockchain design principles, like the emergence of Internet. The development of cross-blockchain applications involves much complexity regarding the variety of underlying cross-blockchain communication. The way to effectively enable interoperability across multiple blockchains is thus essential and expecting to confront various unprecedented challenges. For instance, due to different transaction structures, ensuring the properties of ACID (Atomicity, Consistency, Isolation, Durability) in transactions processing and verification processes across diverse blockchain systems remains a challenging task in both academia and industry. This paper provides a systematic and comprehensive review of the current progress of blockchain interoperability. We explore both general principles and practical schemes to achieve interoperable blockchain systems. We then survey and compare the state-of-the-art solutions to deal with the interoperability of blockchains in detail. Finally, we discuss several critical challenges and some potential research directions to advance the research on exploring blockchain interoperability.

Network Function Virtualisation (NFV) advances the adoption of composable software middleboxes. Accordingly, cloud data centres become major NFV vendors for enterprise traffic processing. Due to the privacy concern of traffic redirection to the cloud, secure middlebox systems (e.g., BlindBox) draw much attention; they can process encrypted packets against encrypted rules directly. However, most of the existing systems supporting pattern matching based network functions require the enterprise gateway to tokenise packet payloads via sliding windows. Such tokenisation induces a considerable communication overhead, which can be over 100$\times$ to the packet size. To overcome this bottleneck, in this paper, we propose the first bandwidth-efficient encrypted pattern matching protocol for secure middleboxes. We resort to a primitive called symmetric hidden vector encryption (SHVE), and propose a variant of it, aka SHVE+, to achieve constant and moderate communication cost. To speed up, we devise encrypted filters to reduce the number of accesses to SHVE+ during matching highly. We formalise the security of our proposed protocol and conduct comprehensive evaluations over real-world rulesets and traffic dumps. The results show that our design can inspect a packet over 20k rules within 100 $\mu$s. Compared to prior work, it brings a saving of 94$\%$ in bandwidth consumption.

Super-spreader events where one person infects many others have been a driving force of the Covid-19 pandemic. Such events often happen indoors, such as in restaurants, at choir practice or in gyms. Many systems for automated contact tracing (ACT) have been proposed, which will warn a user when they have been in proximity to an infected person. These generally fail to detect potential super-spreader events as only users who have come in close contact with the infected person, but not others who also visited the same location, are warned. Other approaches allow users to check into locations or venues, but these require user interaction. We propose two designs how broadcast-based ACT systems can be enhanced to utilize location-specific information without the need for GPS traces or scanning of QR codes. This makes it possible to alert attendees of a potential super-spreader event while still remaining private. Our first design relies on cooperating lighthouses which cover a large area and send out pseudonyms. These are recorded by visitors and published by the health authority (HA) in case of an infection. The second design has lighthouses actively communicating with HAs after retrospectively detecting an infected visitor to warn everyone whose stay overlapped.

There is a difference between a system having no known attacks and the system being secure---as cryptographers know all too well. Once we begin talking about the implementations of systems this issue becomes even more prominent since the amount of material which needs to be scrutinised skyrockets. Historically, lack of transparency and low standards for e-voting system implementations have resulted in a culture where open source code is seen as a gold standard; however, this ignores the issue of the comprehensibility of that code. In this work we make concrete empirical recommendations based on our, and others, experiences and findings from examining the source code of e-voting systems. We highlight that any solution used for significant elections should be well designed, carefully analysed, deftly built, accurately documented and expertly maintained. Until e-voting system implementations are clear, comprehensible, and open to public scrutiny security standards are unlikely to improve.

Leakage assessment of cryptographic implementations with side-channel analysis relies on two important assumptions: leakage model and the number of side-channel traces. In the context of profiled side-channel attacks, having these assumptions correctly defined is a sufficient first step to evaluate the security of a crypto implementation with template attacks. This method assumes that the features (leakages or points of interest) follow a univariate or multi-variate Gaussian distribution for the estimation of the probability density function. When trained machine learning or neural network models are employed as classifiers for profiled attacks, a third assumption must be taken into account that it the correctness of the trained model or learning parameters. It was already proved that convolutional neural networks have advantages for side-channel analysis like bypassing trace misalignments and defeating first-order masking countermeasures in software implementations. However, if this trained model is incorrect and the test classification accuracy is close to random guessing, the correctness of the two first assumptions (number of traces and leakage model) will be insufficient and the security of the target under evaluation can be overestimated. This could lead to wrong conclusions in leakage certifications. One solution to verify if the trained model is acceptable relies on the identifying of input features that the neural network considers as points of interest. In this paper, we implement the assessment of neural network models by using the proposed backward propagation path method. Our method is employed during the profiling phase as a tool to verify what the neural network is learning from side-channel traces and to support the optimization of hyper-parameters. The method is tested against masked AES implementation. One of the main results highlights the importance of L2 regularization for the automated points of interest selection from a neural network.

The cloud changed the way we manage and store data. Today, cloud storage services offer clients an infrastructure that allows them a convenient source to store, replicate, and secure data online. However, with these new capabilities also come limitations, such as lack of transparency, limited decentralization, and challenges with privacy and security. And, as the need for more agile, private and secure data solutions continues to grow exponentially, rethinking the current structure of cloud storage is mission-critical for enterprises. By leveraging and building upon blockchain’s unique attributes, including immutability, security to the data element level, distributed (no single point of failure), we have developed a solution prototype that allows data to be reliably stored while simultaneously being secured, with tamper-evident auditability, via blockchain. The result, Audita, is a flexible solution that assures data protection and solves challenges such as scalability and privacy. Audita works via an augmented blockchain network of participants that include storage-nodes and block-creators. In addition, it provides an automatic and fair challenge system to assure that data is distributed and reliably and provably stored. While the prototype is built on Quorum, the solution framework can be used with any blockchain platform. The benefit is a system that is built to grow along with the data needs of enterprises, while continuing to build the network via incentives and solving for issues such as auditing and outsourcing.

Sharding technology is becoming a promising candidate to address the scalability issues in blockchain. The key concept behind sharding technology is to partition the network status into multiple distinct smaller committees, each of which handles a disjoint set of transactions to leverage its capability of parallel processing. However, when introducing sharding technology to blockchain, several key challenges need to be resolved, such as security and heterogeneity among the participating nodes. This paper introduces RepShard, a reputation-based blockchain sharding scheme that aims to achieve both linearly scaling efficiency and system security simultaneously. RepShard adopts a two-layer hierarchical chain structure, consisting of a reputation chain and independent transaction chains. Each transaction chain is maintained within its shard to record transactions, while the reputation chain is maintained by all shards to update the reputation score of each participating node. We leverage a novel reputation scheme to record each participating node's integrated and valid contribution to the system, in which we consider the heterogeneity of participating nodes (e.g., computational resources). The reputation score used in sharding and leader election processes maintains the balance and security of each shard. RepShard relies on verifiable relay transactions for cross-shard transactions to ensure consistency between distinct shards. By integrating reputation into the sharding protocol, our scheme can offer both scalability and security at the same time.

Process industries cover a wide set of industries, in which the processes are controlled by a combination of Distributed Control Systems (DCSs) and Programmable Logic Controllers (PLCs). These control systems utilize various measurements such as pressure, flow, and temperature to determine the state of the process and then use field devices such as valves and other actuating devices to manipulate the process. Since there are many different types of field devices and since each device is calibrated to its specific installation, when monitoring devices, it is important to be able to transfer not only the device measurement and diagnostics, but also characteristics about the device and the process in which it is installed. The current monitoring architecture however creates challenges for continuous monitoring and analysis of diagnostic data. In this paper, we present the design of an Industrial IoT system for supporting large-scale and continuous device condition monitoring and analysis in process control systems. The system design seamlessly integrates existing infrastructure (e.g., HART and WirelessHART networks, and DeltaV DCS) and newly developed hardware/software components (e.g., one-way data diode, IoT cellular architecture) together for control network data collection and streaming of the collected device diagnostic parameters to a private cloud to perform streaming data analytics designed for fault identification and prediction. A prototype system has been developed and supported by Emerson Automation Solutions and deployed in the field for design validation and long-term performance evaluation. To the best of our knowledge, this is the first ever publicly reported effort on IoT system design for process automation applications. The design can be readily extended for condition monitoring and analysis of many other industrial facilities and processes.

Recently, a light-weight authenticated key-exchange (AKE) scheme has been proposed. The scheme provides mutual authentication. It is asymmetric in nature by delegating complex cryptographic operations to resource-equipped servers, and carefully managing the workload on resource-constrained Smart meter nodes by using Physically Unclonable Functions. The prototype Smart meter built using commercial-off-the-shelf products is enabled with a low-cost countermeasure against load-modification attacks, which goes side-by-side with the proposed protocol. An attack against this AKE scheme has been recently proposed claiming that the server can be breached to mount spoofing attacks. It relies on the assumption that the result of an attack against authenticated key-exchange protocol is determined before the attacker learns the session key. In this short paper, we discuss the attack’s validity and describe the misinterpretation of the AKE protocol’s security definition.

Payment channel networks have been introduced to mitigate the scalability issues inherent to permissionless decentralized cryptocurrencies such as Bitcoin. Launched in 2018, the Lightning Network (LN) has been gaining popularity and consists today of more than 5000 nodes and 30000 payment channels that jointly hold 895 bitcoins (7.6M USD as of February 2020). This adoption has motivated research from both academia and industry. Payment channels suffer from security vulnerabilities, such as the wormhole attack, anonymity issues, and scalability limitations related to the upper bound on the number of concurrent payments per channel, which have been pointed out by the scientific community but never quantitatively analyzed. In this work, we first analyze the proneness of the LN to the wormhole attack and attacks against anonymity. We observe that an adversary needs to control only 2% of LN nodes to learn sensitive payment information (e.g., sender, receiver and payment amount) or to carry out the wormhole attack. Second, we study the management of concurrent payments in the LN and quantify its negative effect on scalability. We observe that for micropayments, the forwarding capability of up to 50% of channels is restricted to a value smaller than the overall channel capacity. This phenomenon not only hinders scalability but also opens the door for DoS attacks: We estimate that a network-wide DoS attack costs within 1.5M USD, while isolating the biggest community from the rest of the network costs only 225k USD. Our findings should prompt the LN community to consider the security, privacy and scalability issues of the network studied in this work when educating users about path selection algorithms, as well as to adopt multi-hop payment protocols that provide stronger security, privacy and scalability guarantees.

Blockchain-based cryptocurrencies offer an appealing alternative to Fiat currencies, due to their decentralized and borderless nature. However the decentralized settings make the authentication process more challenging: Standard cryptographic methods often rely on the ability of users to reliably store a (large) secret information. What happens if one user's key is lost or stolen? Blockchain systems lack of fallback mechanisms that allow one to recover from such an event, whereas the traditional banking system has developed and deploys quite effective solutions. In this work, we develop new cryptographic techniques to integrate security policies (developed in the traditional banking domain) in the blockchain settings. We propose a system where a smart contract is given the custody of the user's funds and has the ability to invoke a two-factor authentication (2FA) procedure in case of an exceptional event (e.g., a particularly large transaction or a key recovery request). To enable this, the owner of the account secret-shares the answers of some security questions among a committee of users. When the 2FA mechanism is triggered, the committee members can provide the smart contract with enough information to check whether an attempt was successful, and nothing more. We then design a protocol that securely and efficiently implements such a functionality: The protocol is round-optimal, is robust to the corruption of a subset of committee members, supports low-entropy secrets, and is concretely efficient. As a stepping stone towards the design of this protocol, we introduce a new threshold homomorphic encryption scheme for linear predicates from bilinear maps, which might be of independent interest. To substantiate the practicality of our approach, we implement the above protocol as a smart contract in Ethereum and show that it can be used today as an additional safeguard for suspicious transactions, at minimal added cost. We also implement a second scheme where the smart contract additionally requests a signature from a physical hardware token, whose verification key is registered upfront by the owner of the funds. We show how to integrate the widely used universal two-factor authentication (U2F) tokens in blockchain environments, thus enabling the deployment of our system with available hardware.

Differential privacy has been widely applied to provide privacy guarantees by adding random noise to the function output. However, it inevitably fails in many high-stakes voting scenarios, where voting rules are required to be deterministic. In this work, we present the first framework for answering the question: ``How private are commonly-used voting rules?" Our answers are two-fold. First, we show that deterministic voting rules provide sufficient privacy in the sense of distributional differential privacy (DDP). We show that assuming the adversarial observer has uncertainty about individual votes, even publishing the histogram of votes achieves good DDP. Second, we introduce the notion of exact privacy to compare the privacy preserved in various commonly-studied voting rules, and obtain dichotomy theorems of exact DDP within a large subset of voting rules called generalized scoring rules.

Federated learning (FL) is an emerging distributed machine learning paradigm which addresses critical data privacy issues in machine learning by enabling clients, using an aggregation server (aggregator), to jointly train a global model without revealing their training data. Thereby, it improves not only privacy but is also efficient as it uses the computation power and data of potentially millions of clients for training in parallel. However, FL is vulnerable to so-called inference attacks by malicious aggregators which can infer information about clients’ data from their model updates. Secure aggregation restricts the central aggregator to only learn the summation or average of the updates of clients. Unfortunately, existing protocols for secure aggregation for FL suffer from high communication, computation, and many communication rounds. In this work, we present SAFELearn, a generic design for efficient private FL systems that protects against inference attacks that have to analyze individual clients' model updates using secure aggregation. It is flexibly adaptable to the efficiency and security requirements of various FL applications and can be instantiated with MPC or FHE. In contrast to previous works, we only need 2 rounds of communication in each training iteration, do not use any expensive cryptographic primitives on clients, tolerate dropouts, and do not rely on a trusted third party. We implement and benchmark an instantiation of our generic design with secure two-party computation. Our implementation aggregates 500~models with more than 300K parameters in less than 0.5 seconds.

Outsourcing computation allows a resource limited client to expand its computational capabilities by outsourcing computation to other nodes or clouds. A basic requirement of outsourcing is providing assurance that the computation result is correct. We consider a smart contract based outsourcing system that achieves assurance by replicating the computation on two servers and accepts the computation result if the two responses match. Correct computation result is obtained by using incentivization to instigate correct behaviour in servers. We show that all previous replication based incentivized outsourcing protocols with proven correctness, fail when automated by a smart contract because of the copy attack where a contractor simply copies the submitted response of the other contractor. We then design an incentivization mechanism that uses two lightweight challenge-response protocols that are used when the submitted results are compared, and employs monetary rewards, fines, and bounties to incentivize correct computation. We use game theory to model and analyze our mechanism, and prove that with appropriate choices of the mechanism parameters, there is a single Nash equilibrium corresponding to the contractors’ strategy of correctly computing the result. Our work provides a foundation for replicated incentivized computation in the smart contract setting and opens new research directions.

Payment channel hubs (PCHs) constitute a promising solution to the inherent scalability problems of blockchain technologies, allowing for off-chain payments between sender and receiver through an intermediary, called the tumbler. While state-of-the-art PCHs provide security and privacy guarantees against a malicious tumbler, they do so by relying on the scripting-based functionality available only at few cryptocurrencies, and they thus fall short of fundamental properties such as backwards compatibility and efficiency. In this work, we present the first PCH protocol to achieve all aforementioned properties. Our PCH builds upon A${^2}$L, a novel cryptographic primitive that realizes a three-party protocol for conditional transactions, where the tumbler pays the receiver only if the latter solves a cryptographic challenge with the help of the sender, which implies the sender has paid the tumbler. We prove the security and privacy guarantees of A${^2}$L (which carry over to our PCH construction) in the Universal Composability framework and present a provably secure instantiation based on adaptor signatures and randomizable puzzles. We implemented A${^2}$L and compared it to TumbleBit, the state-of-the-art Bitcoin-compatible PCH. Asymptotically, A${^2}$L has a communication complexity that is constant, as opposed to linear in the security parameter like in TumbleBit. In practice, A${^2}$L requires $\sim33$x less bandwidth than TumleBit, while retaining the computational cost (or providing $2$x speedup with a preprocessing technique). This demonstrates that A${^2}$L (and thus our PCH construction) is ready to be deployed today. In theory, we demonstrate for the first time that it is possible to design a secure and privacy-preserving PCH while requiring only digital signatures and timelock functionality from the underlying scripting language. In practice, this result makes our PCH backwards compatible with virtually all cryptocurrencies available today, even those offering a highly restricted form of scripting language such as Ripple or Stellar. The practical appealing of our construction has resulted in a proof-of-concept implementation in the COMIT Network, a blockchain technology focused on cross-currency payments.

Adaptor signatures (AS) are an extension of digital signatures that enable the encoding of a cryptographic hard problem (e.g., discrete logarithm) within the signature itself. An AS scheme ensures that (i) the signature can be created only by the user knowing the solution to the cryptographic problem; (ii) the signature reveals the solution itself; (iii) the signature can be verified with the standard verification algorithm. These properties have made AS a salient building block for many blockchain applications, in particular, off-chain payment systems such as payment-channel networks, payment-channel hubs, atomic swaps or discrete log contracts. Current AS constructions, however, are not secure against adversaries with access to a quantum computer. In this work, we present IAS, a construction for adaptor signatures that relies on standard cryptographic assumptions for isogenies, and builds upon the isogeny-based signature scheme CSI-FiSh. We formally prove the security of IAS against a quantum adversary. We have implemented IAS and our evaluation shows that IAS can be incorporated into current blockchains while requiring $\sim1500$ bytes of storage size on-chain and $\sim140$ milliseconds for digital signature verification. We also show how IAS can be seamlessly leveraged to build post-quantum off-chain payment applications without harming their security and privacy.

We propose HERMES, a scalable, secure, and privacy-enhancing system for users to share and access vehicles. HERMES securely outsources operations of vehicle access token generation to a set of untrusted servers. It builds on an earlier proposal, namely SePCAR [1], and extends the system design for improved efficiency and scalability. To cater to system and user needs for secure and private computations, HERMES utilizes and combines several cryptographic primitives with secure multiparty computation efficiently. It conceals secret keys of vehicles and transaction details from the servers, including vehicle booking details, access token information, and user and vehicle identities. It also provides user accountability in case of disputes. Besides, we provide semantic security analysis and prove that HERMES meets its security and privacy requirements. Last but not least, we demonstrate that HERMES is efficient and, in contrast to SePCAR, scales to a large number of users and vehicles, making it practical for real-world deployments. We build our evaluations with two different multiparty computation protocols: HtMAC-MiMC and CBC-MAC-AES. Our results demonstrate that HERMES with HtMAC-MiMC requires only approx 1,83 ms for generating an access token for a single-vehicle owner and approx 11,9 ms for a large branch of rental companies with over a thousand vehicles. It handles 546 and 84 access token generations per second, respectively. This results in HERMES being 696 (with HtMAC-MiMC) and 42 (with CBC-MAC-AES) times faster compared to in SePCAR for a single-vehicle owner access token generation. Furthermore, we show that HERMES is practical on the vehicle side, too, as access token operations performed on a prototype vehicle on-board unit take only approx 62,087 ms.

The ever-growing size of the Bitcoin UTXO state is a factor preventing nodes with limited storage capacity from validating transactions. Cryptographic accumulators, such as Merkle trees, offer a viable solution to the problem. Full nodes create a Merkle tree from the UTXO set, while stateless nodes merely store the root of the Merkle tree. When provided with a proof, stateless nodes can verify that a transaction's inputs belong to the UTXO set. In this work, we present a systematic study of Merkle tree based accumulators, with a focus on factors that reduce the proof size. Based on the observation that UTXOs typically have a short lifetime, we propose that recent UTXOs be co-located in the tree. When proofs for different transactions are batched, such a design reduces the per-transaction proof size. We provide details of our implementation of this idea, describing certain optimizations that further reduce the proof size in practice. On Bitcoin data before August 2019, we show that our design achieves a 4.6x reduction in proof size vis-a-vis UTREEXO [Dryja 2019], which is a different Merkle-tree based system designed to support stateless nodes.

Efficient rank estimation algorithms are of prime interest in security evaluation against side-channel attacks (SCA) and recently also for password strength estimators. In a side channel setting it allows estimating the remaining security after an attack has been performed, quantified as the time complexity and the memory consumption required to brute force the key given the leakages as probability distributions over $d$ subkeys (usually key bytes). In password strength estimators the rank estimation allows estimating how many attempts a password cracker would need until it finds a given password. We propose ESrank, the first rank estimation algorithm with a bounded error ratio: its error ratio is bounded by $\gamma^{2d-2}$, for any probability distribution, where $d$ is the number of subkey dimensions and $\gamma>1$ can be chosen according to the desired accuracy. ESrank is also the first rank estimation algorithm that enjoys provable poly-logarithmic time- and space-complexity. Our main idea is to use exponential sampling to drastically reduce the algorithm's complexity. We evaluated the performance of ESrank on real SCA and password strength corpora. We show ESrank gives excellent rank estimation with roughly a 1-bit margin between lower and upper bounds in less than 1 second on the SCA corpus and 4 seconds preprocessing time and 7$\mu$sec lookup time on the password strength corpus.

Modern SoC designs include several reset domains that enable asynchronous partial resets while obviating complete system boot. Unfortunately, asynchronous resets can introduce security vulnerabilities that are difficult to detect through traditional validation. In this paper, we address this problem through a new security validation framework, SoCCCAR, that accounts for asynchronous resets. The framework involves (1) efficient extraction of reset-controlled events while avoiding combinatorial explosion, and (2) concolic testing for systematic exploration of the extracted design space. Our experiments demonstrate that SoCCAR can achieve almost perfect detection accuracy and verification time of a few seconds on realistic SoC designs.

In the permissionless regime of distributed computing, participants may join and leave an internet-scale protocol execution at will. The permissionless regime poses challenges to the classical techniques used for consensus protocols, in which participants attempt to agree on a function of their inputs. For example, classical consensus techniques require bounding the numbers of honest and corrupt participants, and for honest participants to remain online throughout. Bitcoin's introduction of Proof of Work enabled dynamic participation by shifting focus from the number of parties to the number of hash puzzles that parties collectively solve, and in turn, enforcing constraints on the blocks sent by honest parties. Other Bitcoin-inspired works have developed Proof of X (PoX) variants to remediate the shortcomings of Proof of Work. We propose a new abstraction called resources and argue that in practice, several PoX variants appear to implement resources. For every resource that a party obtains, it is permitted to send a special protocol message. We show that given few additional assumptions, resources are sufficient to achieve consensus in the permissionless regime, even in the presence of a full-information adversary that can choose which parties get resources and when they get them. In particular, it is not necessary to know a bound on the network delay, participants do not need clocks, and participants can join and leave the execution arbitrarily, even after sending only a single message. We require only a known upperbound on the rate at which resources enter the system, relative to the maximum network delay (without needing to know the network delay), and that over the long term, a majority of resources are acquired by honest participants. Our protocol for consensus in the permissionless model follows from a protocol for graph consensus, which we define as a generalization of blockchains. Our graph consensus works even when resources enter the system at high rates, but the required honest majority increases with the rate. We show how to modify the protocol slightly to achieve one-bit consensus. Finally, we show that for every graph consensus protocol that outputs a majority of honest vertices there exists a one-bit consensus protocol.

We present a novel approach for blockchain asset owners to reclaim their funds in case of accidental private-key loss or transfer to a mistyped address. Our solution can be deployed upon failure or absence of proactively implemented backup mechanisms, such as secret sharing and cold storage. The main advantages against previous proposals is it does not require any prior action from users and works with both single-key and multi-sig accounts. We achieve this by a 3-phase Commit() -> Reveal() -> Claim() - or - Challenge() smart contract that enables accessing funds of addresses for which the spending key is not available. We provide an analysis of the threat and incentive models and formalize the concept of reactive KEy-Loss Protection (KELP).

We study the communication complexity of unconditionally secure multiparty computation (MPC) protocols in the honest majority setting. Despite tremendous efforts in achieving efficient protocols for binary fields under computational assumptions, there are no efficient unconditional MPC protocols in this setting. In particular, there are no $n$-party protocols with constant overhead admitting communication complexity of $O(n)$ bits per gate. Cascudo, Cramer, Xing and Yuan (CRYPTO 2018) were the first ones to achieve such an overhead in the amortized setting by evaluating $O(\log n)$ copies of the same circuit in the binary field in parallel. In this work, we construct the first unconditional MPC protocol secure against a malicious adversary in the honest majority setting evaluating just a single boolean circuit with amortized communication complexity of $O(n)$ bits per gate.

Enforcement of policy regulations and availability of auditing mechanisms are crucial building blocks for the adoption of distributed payment systems. This paper reviews a number of existing proposals for distributed payment systems that offer some form of auditability for regulators. We identify two major distinct lines of work: payment systems that are not privacy-preserving such as Bitcoin, where regulation functionalities are typically tailored for organizations controlling many accounts, and privacy-preserving payment systems where regulation functionalities are typically targeted to user level. We provide a systematization methodology over several axes of characteristics and performance, while highlighting insights and research gaps that we have identified, such as lack of dispute-resolution solutions between the regulator and the entity under audit, and the incompatibility of ledger pruning or off-chain protocols with regulatory requirements. Based on our findings, we propose a number of exciting future research directions.

In this paper we extend the attack landscape of bribing attacks on cryptocurrencies by presenting a new method, which we call Pay-To-Win (P2W). To the best of our knowledge, it is the first approach capable of facilitating double-spend collusion across different blockchains. Moreover, our technique can also be used to specifically incentivize transaction exclusion or (re)ordering. For our construction we rely on smart contracts to render the payment and receipt of bribes trustless for the briber as well as the bribee. Attacks using our approach are operated and financed out-of-band i.e., on a funding cryptocurrency, while the consequences are induced in a different target cryptocurrency. Hereby, the main requirement is that smart contracts on the funding cryptocurrency are able to verify consensus rules of the target. For a concrete instantiation of our P2W method, we choose Bitcoin as a target and Ethereum as a funding cryptocurrency. Our P2W method is designed in a way that reimburses collaborators even in the case of an unsuccessful attack. Interestingly, this actually renders our approach approximately one order of magnitude cheaper than comparable bribing techniques (e.g., the whale attack). We demonstrate the technical feasibility of P2W attacks through publishing all relevant artifacts of this paper, ranging from calculations of success probabilities to a fully functional proof-of-concept implementation, consisting of an Ethereum smart contract and a Python client.

A long standing question in the context of cryptocurrencies based on Nakamoto consensus is whether such constructions are incentive compatible, i.e., the intended properties of the system emerge from the appropriate utility model for participants. Bribing and other related attacks, such as front-running or Goldfinger attacks, aim to directly influence the incentives of actors within (or outside) of the targeted cryptocurrency system. The theoretical possibility of bribing at tacks on cryptocurrencies was discussed early on in the cryptocurrency community and various different techniques and approaches have since been proposed. Some of these attacks are designed to gain in-band profits, while others intend to break the mechanism design and render the cryptocurrency worthless. In this paper, we systematically expose the large but scattered body of research in this area which has accumulated over the years. We summarize these bribing attacks and similar techniques that leverage on programmatic execution and verification under the term algorithmic incentive manipulation (AIM) attacks, and show that the problem space is not yet fully explored. Based on our analysis we present several research gaps and opportunities that warrant further investigation. In particular, we highlight no- and near-fork attacks as a powerful, yet largely underestimated, AIM category that raises serious security concerns not only for smart contract platforms.

Current designs of Online Social Networks (OSN) deploy centralized architecture where a central OSN provider handles all the users’ read and write requests over the shared data (e.g., Facebook wall or a group page). The historical incidents demonstrate that such centralization is leveraged for censorship and violating view consistency; a corrupted provider deliberately displays different views of the shared data to the users. Integrita provides a data-sharing mechanism that protects view consistency by replacing the centralized architecture with the federated-server model consisting of N malicious providers, N − 1 of which can be colluding. The state of the shared data is modeled by an append-only data structure, stored at the servers side, which contains the history of all the operations performed by the users. The consistency of users’ views towards shared data depends on their accessibility to the intact log of operations. Integrita guarantees that the servers cannot manipulate the log without being detected by the users. Unlike the state-of-the-art, Integrita accomplishes this neither by using storage inefficient data replication nor by requiring users to exchange their views. Every user, without relying on the presence of other users, can verify whether his operation has been added to the log and is visible to the rest of the users. We introduce and achieve a new level of view consistency named q-detectable consistency, where any inconsistency between users’ views cannot remain undetected for more than q operations where q is a function of the number of the servers. This level of consistency is stronger than what centralized counterparts offer. Also, our proposal reduces the storage overhead imposed by replication-based solutions by the multiplicative factor of 1/N. Furthermore, the application of Integrita is not limited to OSNs, and can be integrated into any log-based systems e.g., versioning control system as well.

Dark markets for share trading provide an important function in the financial services industry, however the operators of such markets are not operating in the dark; leaving the possibility open for the market operator to gain a market advantage. Multi-Party Computation (MPC) is a method to remove the need for such trusted third parties. In this paper we show how MPC can be used to implement such a dark market. Importantly we base our solution on the throughputs and algorithms needed in real markets. In particular we base our implementation on the Turquoise Plato market used by the London Stock Exchange.

Security and Scalability are two major challenges that IoT is currently facing. Access control to critical IoT infrastructure is considered as top security challenge that IoT faces. Data generated by IoT devices may be driving many hard real time systems, thus it is of utmost importance to guarantee integrity and authenticity of the data and resources at the first place itself. Due to heterogeneous and constrained nature of IoT devices, traditional IoT security frameworks are not able to deliver scalable, efficient and manageable mechanisms to meet the requirements of IoT devices. On the other hand Blockchain technology has shown great potential to bridge the missing gap towards building a truly decentralized, trustworthy, secure and scalable environment for IoT. Allowing access to IoT resources and data managed through Blockchain will provide an additional security layer backed by the strongest cryptographic algorithms available. In this work we present a reputation driven dynamic access control framework for small scale IoT applications based on Proof of Authority Blockchain, we name it as Rep-ACM. In RepACM framework we build two major services, one for Reputation building (for better IoT device behaviour regulations) and other for Misbehaviour detection (for detecting any Misbehaviour on object resource usage). Both of these services work in coordination with other services of proposed framework to determine who can access what and under what conditions access should be granted. For Proof of Concept (PoC) we created private Ethereum network consisting of two Raspberry Pi single board computers, one desktop computer and a laptop as nodes. We configured Ethereum protocol to use Istanbul Byzantine Fault Tolerance (IBFT) as Proof of Authority (PoA) consensus mechanism for performance optimization in constrained environment. We deployed our model on private network for feasibility and performance analysis.

Distributed ledgers implement a storage layer, on top of which a shared state is maintained in a decentralized manner. In UTxO-based ledgers, like Bitcoin, the shared state is the set of all unspent outputs (UTxOs), which serve as inputs to future transactions. The continuously increasing size of this shared state will gradually render its maintenance unaffordable. Our work investigates techniques that minimize the shared state of the distributed ledger, i.e., the in-memory UTxO set. To this end, we follow two directions: a) we propose novel transaction optimization techniques to be followed by wallets, so as to create transactions that reduce the shared state cost and b) propose a novel fee scheme that incentivizes the creation of "state-friendly" transactions. We devise an abstract ledger model, expressed via a series of algebraic operators, and define the transaction optimization problem of minimizing the shared state; we also propose a multi-layered algorithm that approximates the optimal solution to this problem. Finally, we define the necessary conditions such that a ledger’s fee scheme incentivizes proper state management and propose a state efficient fee function for Bitcoin.

Loyalty programs in the form of punch cards that can be redeemed for benefits have long been a ubiquitous element of the consumer landscape. However, their increasingly popular digital equivalents, while providing more convenience and better bookkeeping, pose a considerable privacy risk. This paper introduces a privacy-preserving punch card protocol that allows firms to digitize their loyalty programs without forcing customers to submit to corporate surveillance. We also present a number of extensions that allow our scheme to provide other privacy-preserving customer loyalty features. Compared to the best prior work, we achieve a $14\times$ reduction in the computation and a $11\times$ reduction in the communication required to perform a ``hole punch,'' a $55\times$ reduction in the communication required to redeem a punch card, and a $128\times$ reduction in the computation time required to redeem a card. Much of our performance improvement can be attributed to removing the reliance on pairings or range proofs present in prior work, which has only addressed this problem in the context of more general loyalty systems. By tailoring our scheme to punch cards and related loyalty systems, we demonstrate that we can reduce communication and computation costs by orders of magnitude.

The Covid-19 pandemic created various new challenges for our societies. Quickly discovering new infections using automated contact tracing without endangering privacy of the general public is one of these. Most discussions concerning architectures for contact tracing applications revolved around centralized against decentralized approaches. In contrast, the system proposed in this work builds on the idea of message-based contact tracing to inform users of their risk. Our main contribution is the combination of a blind-signature approach to verify infections with an anonymous postbox service. In our evaluation we analyze all components in our system for performance and privacy, as well as security. We derive parameters for operating our system in a pandemic scenario.

To combat the ongoing Covid-19 pandemic, many new ways have been proposed on how to automate the process of finding infected people, also called contact tracing. A special focus was put on preserving the privacy of users. Bluetooth Low Energy (BLE) as base technology has the most promising properties, so this survey focuses on automated contact tracing techniques using BLE. We define multiple classes of methods and identify two major groups: systems that rely on a server for finding new infections and systems that distribute this process. Existing approaches are systematically classified regarding security and privacy criteria.

End-user privacy in mobile telephony systems is nowadays of great interest because of the envisaged hyper-connectivity and the potential of the unprecedented services (virtual reality, machine-type communication, vehicle-to-everything, IoT, etc.) being offered by the new 5G system. This paper reviews the state of subscription privacy in 5G systems. As the work on 5G Release 15 -- the first full set of 5G standards -- has recently been completed, this seems to be an appropriate occasion for such a review. The scope of the privacy study undertaken is limited to the wireless part of the 5G system which occurs between the service provider's base station and the subscriber's mobile phone. Although 5G offers better privacy guarantees than its predecessors, this work highlights that there still remain significant issues which need rectifying. We undertook an endeavor to (i) compile the privacy vulnerabilities that already existed in the previous mobile telephony generations. Thereafter, (ii) the privacy improvements offered by the recently finalized 5G standard were aggregated. Consequently, (iii) we were able to highlight privacy issues from previous generations that remain unresolved in 5G Release 15. For completeness, (iv) we also explore new privacy attacks which surfaced after the publication of the 5G standard. To address the identified privacy gaps, we also present future research directions in the form of proposed improvements.

In this paper we consider ``OR" based monochrome random grid visual cryptographic schemes (RGVCS) for $t$-$(k,n)^*$ access structure which is a generalization of the threshold $(k,n)$ access structure in the sense that in all the successful attempts to recover the secret image, the $t$ essential participants must always be present. Up to the best of our knowledge, the current proposed work is the first in the literature of RGVCS which provides efficient direct constructions for the $t$-$(k,n)^*$-RGVCS for ``OR" based model. Finding the closed form of light contrast is a challenging work. However, in this paper we come up with the closed form of the light contrast for the ``OR" based model. In literature, there are visual cryptographic schemes where the secret reconstruction is done by binary ``XOR" operation instead of ``OR" operation to increase the relative contrast of the decoded image. In this paper, we also propose an extended grid based $t$-$(k,n)^*$-RGVCS in which we replace the traditional ``OR" operation by ``XOR" operation. Note that the use of XOR operation indicates that the decoding must be performed computationally and not visually. We justified our schemes using both experimental as well as simulation based data.

Resource Public Key Infrastructure (RPKI) is vital to the security of inter-domain routing. However, RPKI enables Regional Internet Registries (RIRs) to unilaterally takedown IP prefixes - indeed, such attacks have been launched by nation-state adversaries. The threat of IP prefix takedowns is one of the factors hindering RPKI adoption. In this work, we propose the first distributed RPKI system, based on threshold signatures, that requires the coordination of a number of RIRs to make changes to RPKI objects; hence, preventing unilateral prefix takedown. We perform extensive evaluations using our implementation demonstrating the practicality of our solution. Furthermore, we show that our system is scalable and remains efficient even when RPKI is widely deployed.

In this paper, we introduce FPPW, a new payment channel with watchtower scheme for Bitcoin. This new scheme provides fairness w.r.t. all channel participants including both channel parties and the watchtower. It means that the funds of any honest channel participant are safe even assuming that other two channel participants are corrupted and/or collude with each other. Furthermore, the watchtower in FPPW learns no information about the off-chain transactions and hence the channel balance privacy is preserved. As a byproduct, we also define the coverage of a watchtower scheme, that is the total capacity of channels that a watchtower can cover on a scale of 0 to 1, and show that FPPW's coverage is higher than those of PISA and Cerberus. The scheme can be implemented without any update in Bitcoin script.

Due to high IC design costs and emergence of countless untrusted foundries, logic encryption has been taken into consideration more than ever. In state-of-the-art logic encryption works, a lot of performance is sold to guarantee security against both the SAT-based and the removal attacks. However, the SAT-based attack cannot decrypt the sequential circuits if the scan chain is protected or if the unreachable states encryption is adopted. Instead, these security schemes can be defeated by the model checking attack that searches iteratively for different input sequences to put the activated IC to the desired reachable state. In this paper, we propose a practical logic encryption approach to defend against the model checking attack on sequential circuits. The robustness of the proposed approach is demonstrated by experiments on around fifty benchmarks.

Gun violence results in a significant number of deaths in the United States. Starting in the 1960’s, the US Congress passed a series of gun control laws to regulate the sale and use of firearms. One of the most important but politically fraught gun control measures is a national gun registry. A US Senate office is currently drafting legislation that proposes the creation of a voluntary national gun registration system. At a high level, the bill envisions a decentralized system where local county officials would control and manage the registration data of their constituents. These local databases could then be queried by other officials and law enforcement to trace guns. Due to the sensitive nature of this data, however, these databases should guarantee the confidentiality of the data. In this work, we translate the high-level vision of the proposed legislation into technical requirements and design a cryptographic protocol that meets them. Roughly speaking, the protocol can be viewed as a decentralized system of locally-managed end-to-end encrypted databases. Our design relies on various cryptographic building blocks including structured encryption, secure multi-party computation and secret sharing. We propose a formal security definition and prove that our design meets it. We implemented our protocol and evaluated its performance empirically at the scale it would have to run if it were deployed in the United States. Our results show that a decentralized and end-to-end encrypted national gun registry is not only possible in theory but feasible in practice.

The security of blockchain based decentralized ledgers relies on consensus protocols executed between mutually distrustful parties. Such protocols incur delays which severely limit the throughput of such ledgers. Payment and state channels enable execution of offchain protocols that allow interaction between parties without involving the consensus protocol. Protocols such as Hashed Timelock Contracts (HTLC) and Sprites (FC'19) connect channels into Payment Channel Networks (PCN) allowing payments across a path of payment channels. Such a payment requires each party to lock away funds for an amount of time. The product of funds and locktime is the collateral of the party, i.e., their cost of opportunity to forward a payment. In the case of HTLC, the locktime is linear to the length of the path, making the total collateral invested across the path quadratic in size of its length. Sprites improved on this by reducing the locktime to a constant by utilizing smart contracts. Atomic Multi-Channel Updates (AMCU), published at CCS'19, introduced constant collateral payments without smart contracts. In this work we present the Channel Closure attack on AMCU that allows a malicious adversary to make honest parties lose funds. Furthermore, we propose the Payment Trees protocol that allows payments across a PCN with linear total collateral without the aid of smart contracts; a competitive performance similar to Sprites, and yet compatible to Bitcoin.

A major concern in training and releasing machine learning models is to what extent the model contains sensitive information that the data holders do not want to reveal. Property inference attacks consider an adversary who has access to the trained model and tries to extract some global statistics of the training data. In this work, we study property inference in scenarios where the adversary can maliciously control part of the training data (poisoning data) with the goal of increasing the leakage. Previous work on poisoning attacks focused on trying to decrease the accuracy of models either on the whole population or on specific sub-populations or instances. Here, for the first time, we study poisoning attacks where the goal of the adversary is to increase the information leakage of the model. Our findings suggest that poisoning attacks can boost the information leakage significantly and should be considered as a stronger threat model in sensitive applications where some of the data sources may be malicious. We first describe our property inference poisoning attack that allows the adversary to learn the prevalence in the training data of any property it chooses: it chooses the property to attack, then submits input data according to a poisoned distribution, and finally uses black box queries (label-only queries) on the trained model to determine the frequency of the chosen property. We theoretically prove that our attack can always succeed as long as the learning algorithm used has good generalization properties. We then verify effectiveness of our attack by experimentally evaluating it on two datasets: a Census dataset and the Enron email dataset. In the first case we show that classifiers that recognizes whether an individual has high income (Census data) also leak information about the race and gender ratios of the underlying dataset. In the second case, we show classifiers trained to detect spam emails (Enron data) can also reveal the fraction of emails which show negative sentiment (according to a sentiment analysis algorithm); note that the sentiment is not a feature in the training dataset, but rather some feature that the adversary chooses and can be derived from the existing features (in this case the words). Finally, we add an additional feature to each dataset that is chosen at random, independent of the other features, and show that the classifiers can also be made to leak statistics about this feature; this shows that the attack can target features completely uncorrelated with the original training task. We were able to achieve above $90\%$ attack accuracy with $9-10\%$ poisoning in all of these experiments.

Credit and debit-card payments are typically authenticated with PINs. Once entered into a terminal, the PIN is sent as an encrypted \emph{PIN block} across a payments network to the destination bank, which decrypts and verifies the PIN block. Each node in the payments network routes the PIN block to the next node by decrypting the block with its own key, and then re-encrypting the PIN block with the next node's key; nodes establish shared secret keys with their neighbors to do so. This decrypt-then-encrypt operation over PIN blocks is known as \emph{PIN translation}, and it is currently performed in Hardware Security Modules (HSMs) to avoid possible PIN exposure. However, HSMs incur heavy acquisition and operational expenses. Introduced at EUROCRYPT'98, proxy re-encryption (PRE) is a cryptographic primitive which can re-encrypt without exposing sensitive data. We perform an extensive study of PRE as applied to PIN translation, and show through formalization, security analysis, and an implementation study that PRE is a practical alternative to HSMs. With PRE, we eliminate the need for HSMs during re-encryption of a PIN, thus greatly reducing the number of HSMs needed by each participant in the payments ecosystem. Along the way we conduct practice-oriented PRE research, with novel theoretical contributions to resolve issues in comparing so-called honest re-encryption to chosen-ciphertext PRE security, and a new efficient PRE scheme achieving a type of chosen-ciphertext security.

In this paper we propose Bitcontracts, a novel solution that enables secure and efficient execution of generic smart contracts on top of unmodified legacy cryptocurrencies like Bitcoin that do not support contracts natively. The starting point of our solution is an off-chain execution model, where the contract's issuers appoints a set of service providers to execute the contract's code. The contract's execution results are accepted if a quorum of service providers reports the same result and clients are free to choose which such contracts they trust and use. The main technical contribution of this paper is how to realize such a trust model securely and efficiently without modifying the underlying blockchain. We also identify a set of generic properties that a blockchain system must support so that expressive smart contracts can be added safely, and analyze popular existing blockchains based on these criteria.

In 2020 Bernstein, De Feo, Leroux, and Smith presented a new odd-degree $\ell$-isogeny computation method called Velusqrt. This method has complexity $\tilde{O}(\sqrt{\ell})$, compared to the complexity of $\tilde{O}(\ell)$ of the classical Vélu method. In this paper application of the Velusqrt method to Huff's and general Huff's curves is presented. It is showed how to compute odd-degree isogeny on Huff's and general Huff's curves using Velusqrt algorithm and $x$-line arithmetic for different compression functions.

We propose, design, and evaluate PIVOT, a privacy-enhancing and effective contact tracing solution that aims to strike a balance between utility and privacy: one that does not collect sensitive information yet allowing effective tracing and notifying the close contacts of diagnosed users. PIVOT requires a considerably lower degree of trust in the entities involved compared to centralised alternatives while retaining the necessary utility. To protect users' privacy, it uses local proximity tracing based on broadcasting and recording constantly changing anonymous public keys via short-range communication. These public keys are used to establish a shared secret key between two people in close contact. The three keys (i.e., the two public keys and the established shared key) are then used to generate two unique per-user-per-contact hashes: one for infection registration and one for exposure score query. These hashes are never revealed to the public. To improve utility, user exposure score computation is performed centrally, which provides health authorities with minimal, yet insightful and actionable data. Data minimisation is achieved by the use of per-user-per-contact hashes and by enforcing role separation: the health authority act as a mixing node, while the matching between reported and queried hashes is outsourced to a third entity, an independent matching service. This separation ensures that out-of-scope information, such as users' social interactions, is hidden from the health authorities, whereas the matching service does not learn users' sensitive information. To sustain our claims, we conduct a practical evaluation that encompasses anonymity guarantees and energy requirements.

Due to the constant increase and versatility of IoT devices that should keep sensitive information private, Side-Channel Analysis (SCA) attacks on embedded devices are gaining visibility in the industrial field. The integration and validation of countermeasures against SCA can be an expensive and cumbersome process, especially for the less experienced ones, and current certification procedures require to attack the devices under test using multiple SCA techniques and attack vectors, often implying a high degree of complexity. The goal of this paper is to ease one of the most crucial and tedious steps of profiling attacks i.e. the points of interest (POI) selection and hence assist the SCA evaluation process. To this end, we introduce the usage of Estimation of Distribution Algorithms (EDAs) in the SCA field in order to automatically tune the point of interest selection. We showcase our approach on several experimental use cases, including attacks on unprotected and protected AES implementations over distinct copies of the same device, dismissing in this way the portability issue.

Every user can be identified online by a unique string used for email or nickname on some of the many platforms out there. IBE systems propose a simple cryptosystem in which the public key system can be omitted by using the unique string as public identification. In this paper we present a minimal email application that uses Clifford Cocks’ proposed IBE scheme. We analyze the impact of using it inside our application and how it can be improved to better fit the need of nowadays applications.

The voting process is fundamental to any democratic system – be it a country or a company's boardroom. Nearly forty years ago, e-voting was theoretically perceived as a more efficient replacement of the widely existing paper-based traditional voting system. Several research works have been carried out to ensure more security and efficiency in different settings for e-voting schemes. One of the fundamental building blocks of e-voting systems is the public Bulletin Board through which several security properties are achieved. After introducing Blockchain technology, the bulletin board has found a new meaningful and concrete way of distributed way of implementation. Before Blockchain technology, either such a system was theoretically assumed or perceived as a public broadcast channel with memory. In this survey, we present a concise survey of bulletin boards' evolution with a typical application to the e-voting systems. We note that bulletin boards have other applications in other joint computation areas. Still, we are interested in evolving e-voting systems based on bulletin board and how several desired security properties are realized through bulletin boards.

Nowadays, genomic sequencing has become much more affordable for many people and, thus, many people own their genomic data in a digital format. Having paid for genomic sequencing, they want to make use of their data for different tasks that are possible only using genomics, and they share their data with third parties to achieve these tasks, e.g., to find their relatives in a genomic database. As a consequence, more genomic data get collected worldwide. The upside of the data collection is that unique analyses on these data become possible. However, this raises privacy concerns because the genomic data uniquely identify their owner, contain sensitive data about his/her risk for getting particular diseases, and even sensitive information about his/her family members. In this paper, we introduce EPISODE - a highly efficient privacy-preserving protocol for Similar Sequence Queries (SSQs), which can be used for finding genetically similar individuals in an outsourced genomic database, i.e., securely aggregated from data of multiple institutions. Our SSQ protocol is based on the edit distance approximation by Asharov et al. (PETS'18), which we further optimize and extend to the outsourcing scenario. We improve their protocol by using more efficient building blocks and achieve a 5-6x run-time improvement compared to their work in the same two-party scenario. Recently, Cheng et al. (ASIACCS'18) introduced protocols for outsourced SSQs that rely on homomorphic encryption. Our new protocol outperforms theirs by more than factor 24000x in terms of run-time in the same setting and guarantees the same level of security. In addition, we show that our algorithm scales for practical database sizes by querying a database that contains up to a million short sequences within a few minutes, and a database with hundreds of whole-genome sequences containing 75 million alleles each within a few hours.

Many web-based and mobile applications and services allow users to indicate their preferences regarding whether and how their personal information can be used or reused by the application itself, by the service provider, and/or by third parties. The number of possible configurations that constitute a user's preference profile can be overwhelming to a typical user. This report describes a practical, privacy-preserving technique for reducing the burden users face when specifying their preferences by offering users data-driven recommendations for fully-specified preference profiles based on their inputs for just a few settings. The feasibility of the approach is demonstrated by a browser-based prototype application that relies on secure multi-party computation and uses the web-compatible JIFF library as the backbone for managing communications between the client application and the recommendation service. The principal algorithms used for generating proposed preference profiles are $k$-means clustering (for privacy-preserving analysis of preference profile data across multiple users) and $k$-nearest neighbors (for selecting a proposed preference profile to recommend to the user).

Distributed ledger technologies provide high availability and integrity, making them a key enabler for practical and secure computation of distributed workloads among mutually distrustful parties. However, many practical applications also require confidentiality, the third pillar of the CIA triad. In this work, we enhance permissioned and permissionless blockchains with the ability to manage confidential data without forfeiting availability or decentralization. More specifically, CALYPSO sets out to achieve two orthogonal goals that challenge modern distributed ledgers: (a) enable blockchains to auditably manage secrets and (b) protect distributed computations against arbitrage attacks when their results depend on the ordering and secrecy of inputs. To this end, CALYPSO proposes on-chain secrets, a novel abstraction that enforces atomic deposition of an auditable trace whenever users access confidential data. Furthermore, CALYPSO provides user-controlled consent management that ensures revocation atomicity and accountable anonymity. Finally, to enable the permissionless deployment of CALYPSO, we introduce an incentive scheme and provide users with the option to select their preferred trustees. We evaluated our CALYPSO prototype with a confidential document sharing application and a decentralized lottery. Our benchmarks show that the latency of processing transactions increases linearly to the added security (in number of trustees) and is in the range of 0.2 to 8 seconds for 16 to 128 trustees.

Recently, research on deep learning based side-channel analysis (DLSCA) has received a lot of attention. Deep learning-based profiling methods similar to template attacks as well as non-profiling-based methods similar to differential power analysis have been proposed. DLSCA methods have been proposed for targets to which masking schemes or jitter-based hiding schemes are applied. However, most of them are methods for finding the secret key, except for methods for preprocessing, and there are no studies on the target to which the dummy-based hiding schemes or shuffling schemes are applied. In this paper, we propose a DLSCA for detecting dummy operations. In the previous study, dummy operations were detected using the method called BCDC, but there is a disadvantage in that it is impossible to detect dummy operations for commercial devices such as an IC card. We consider the detection of dummy operations as a multi-label classification problem and propose a deep learning method based on CNN to solve it. As a result, it is possible to successfully perform detection of dummy operations on an IC card, which was not possible in the previous study.

Sharding is an approach to designing a highly scalable blockchain. A sharded blockchain achieves parallelism by dividing consensus nodes (validators) into groups called shards and making them process different transactions in each shard. In this paper, we economically analyze users’ behavior on sharded blockchains and identify a phenomenon that users’ accounts and smart contracts eventually get concentrated in a few shards, making shard loads unfair. This phenomenon leads to bad user experiences, such as delays in transaction inclusions and increased transaction fees. To solve the above problem, we propose a load balancing framework in sharded blockchains in which accounts and contracts are frequently reassigned into shards to reduce the difference of loads between shards. We formulate the contract reassignment as an optimization problem and present the algorithm to solve it. Further, we apply the framework to an existing sharding design (Ethereum 2.0) and modify the protocol to do load balancing. Finally, we simulate the protocol and observe smaller transaction delays and fees.

Talek is a private group messaging system that sends messages through potentially untrustworthy servers, while hiding both data content and the communication patterns among its users. Talek explores a new point in the design space of private messaging; it guarantees access sequence indistinguishability, which is among the strongest guarantees in the space, while assuming an anytrust threat model, which is only slightly weaker than the strongest threat model currently found in related work. Our results suggest that this is a pragmatic point in the design space, since it supports strong privacy and good performance: we demonstrate a 3-server Talek cluster that achieves throughput of 9,433 messages/second for 32,000 active users with 1.7-second end-to-end latency. To achieve its security goals without coordination between clients, Talek relies on information-theoretic private information retrieval. To achieve good performance and minimize server-side storage, Talek intro- duces new techniques and optimizations that may be of independent interest, e.g., a novel use of blocked cuckoo hashing and support for private notifications. The latter provide a private, efficient mechanism for users to learn, without polling, which logs have new messages.