We present EDRAX, an architecture for cryptocurrencies with stateless transaction validation. In EDRAX, miners and validating nodes process transactions and blocks simply by accessing a short commitment of the current state found in the most recent block. Therefore there is no need to store off-chain and on-disk, order-of-gigabytes large validation state. We present two instantiations of EDRAX, one in the UTXO model and one in the accounts model. Our UTXO instantiation uses sparse Merkle trees, which are very fast and require no trusted setup. Our accounts instantiation uses a distributed vector commitment, a type of vector commitment that has state-independent updates, meaning it can be synchronized by accessing only update data (e.g., send 5 ETH from Alice to Bob). Towards this goal, we build a new succinct distributed vector commitment based on multiplexer polynomials and zk-SNARKs, that scales up to one billion accounts. We perform an extensive experimental evaluation comparing to other (recently) proposed approaches for stateless transaction validation, showing that sparse Merkle trees and our new distributed vector commitment offer excellent tradeoffs in this application domain.

To effectively trace the infection spread in a pandemic, a large number of manual contact tracers are required to reach out to all possible contacts of infected users. Exposure notification, a.k.a. digital contact tracing, can supplement manual contact tracing to ease the burden on manual tracers and to digitally obtain accurate contact information. We review the state-of-the-art solutions that offer security and privacy-friendly design. We study the role of policies and decision making to implement exposure notification and to protect user privacy. We then study how risk emerges in security, privacy, architecture, and technology aspects of exposure notification systems, and we wrap up with a discussion on architecture aspects to support these solutions.

A decentralized funding system that supports companies of online products through mining cryptocurrencies and which renders mining pools benign. Working in tandem with blockchain cryptocurrencies, the system utilizes a user’s computing power to mine cryptocurrencies and future blockchain technologies. The system mines cryptocurrencies through a machine’s hardware during periods of low usage from the user. The blockchain payments received from the mining will be divvied between the services the user accesses via a percentage of use. A layer of blockchain technology is added to authenticate companies of online products and confirm the wallets of these companies. Each block contains the online service wallet’s public key for approved cryptocurrencies, a form of communication, and a DNS to confirm transmissions to the correct online service. After widespread adoption, disputes of DNS registration will result in the oldest block being the legitimate owner. Online services registered would be responsible for updating the blockchain. As the decentralized network of machines grows, the threat of manipulation through the 51% attack decreases as large mining pools lose the percentage of mining they have.

Genome-Wide Association Studies (GWAS) seek to identify genetic variants associated with a trait, and have been a powerful approach for understanding complex diseases. A critical challenge for GWAS has been the dependence on individual-level data that typically have strict privacy requirements, creating an urgent need for methods that preserve the individual-level privacy of participants. Here, we present a privacy-preserving framework based on several advances in homomorphic encryption and demonstrate that it can perform an accurate GWAS analysis for a real dataset of more than 25,000 individuals, keeping all individual data encrypted and requiring no user interactions. Our extrapolations show that it can evaluate GWAS of 100,000 individuals and 500,000 SNPs in 5.6 hours on a single server node (or in 11 minutes on 31 server nodes running in parallel). Our performance results are more than one order of magnitude faster than prior state-of-the-art results using secure multi-party computation, which requires continuous user interactions, with the accuracy of both solutions being similar. Our homomorphic encryption advances can also be applied to other domains where large-scale statistical analyses over encrypted data are needed.

Today, deep neural networks are a common choice for conducting the profiled side-channel analysis. Such techniques commonly do not require pre-processing, and yet, they can break targets protected with countermeasures. Unfortunately, it is not trivial to find neural network hyper-parameters that would result in such top-performing attacks. The hyper-parameter leading the training process is the number of epochs during which the training happens. If the training is too short, the network does not reach its full capacity, while if the training is too long, the network overfits, and is not able to generalize to unseen examples. Finding the right moment to stop the training process is particularly difficult for side-channel analysis as there are no clear connections between machine learning and side-channel metrics that govern the training and attack phases, respectively. In this paper, we tackle the problem of determining the correct epoch to stop the training in deep learning-based side-channel analysis. We explore how information is propagated through the hidden layers of a neural network, which allows us to monitor how training is evolving. We demonstrate that the amount of information, or, more precisely, mutual information transferred to the output layer, can be measured and used as a reference metric to determine the epoch at which the network offers optimal generalization. To validate the proposed methodology, we provide extensive experimental results that confirm the effectiveness of our metric for avoiding overfitting in the profiled side-channel analysis.

In this paper, we review different approaches on proximity tracing apps which are supposed to automate the current labor-intensive contact tracing approach conducted by national health officials. The purpose of these apps is to automatically notify people who are at risk of being infected with SARS-CoV-2 to interrupt infection chains as early as possible. However, a privacy-preserving and yet functional and scalable design of such apps is not trivial and in some parts leads to counter-intuitive properties. This paper reviews the most prominent European approaches, DP-3T, the German variant "NTK"' of PEPP-PT, and its closely related concept ROBERT. We discuss their design decisions from a privacy perspective and point out the fundamentally different adversary models assumed by the approaches. In addition, we touch on practical aspects such as scalability and ease of implementation.

Collateral employed in cryptoeconomic protocols protects against the misbehavior of economically rational agents, compensating honest users for damages and punishing misbehaving parties. The introduction of collateral, however, carries three disadvantages: (i) requiring agents to lock up a substantial amount of collateral can be an entry barrier, limiting the set of candidates to wealthy agents; (ii) affected agents incur ongoing opportunity costs as the collateral cannot be utilized elsewhere; and (iii) users wishing to interact with an agent on a frequent basis (e.g., with a service provider to facilitate second-layer payments), have to ensure the correctness of each interaction individually instead of subscribing to a service period in which interactions are secured by the underlying collateral. We present Promise, a subscription mechanism to decrease the initial capital requirements of economically rational service providers in cryptoeconomic protocols. The mechanism leverages future income (such as service fees) prepaid by users to reduce the collateral actively locked up by service providers, while sustaining secure operation of the protocol. Promise is applicable in the context of multiple service providers competing for users. We provide a model for evaluating its effectiveness and argue its security. Demonstrating Promise's applicability, we discuss how Promise can be integrated into a cross-chain interoperability protocol, XCLAIM, and a second-layer scaling protocol, NOCUST. Last, we present an implementation of the protocol on Ethereum showing that all functions of the protocol can be implemented in constant time complexity and Promise only adds USD 0.05 for a setup per user and service provider and USD 0.01 per service delivery during the subscription period.

The COVID-19 pandemic created a noticeable challenge to the cryptographic community with the development of contact tracing applications. The media reported a dispute between designers proposing a centralized or a decentralized solution (namely, the PEPP-PT and the DP3T projects). Perhaps, the time constraints to develop and deploy efficient solutions led to non-optimal (in terms of privacy) solutions. Moreover, arguments have been severely biased and the scientific debate did not really happen until recently. In this paper, we show the vulnerabilities and the advantages of both solutions systematically. We believe that none offers any sufficient level of privacy protection and the decision to use one or another is as hard as using automated contact tracing at the first place. A third way could be explored. We list here a few possible directions.

Mix nets are often used to provide privacy in modern security protocols, through shuffling. Some of the most important applications, such as secure electronic voting, require mix nets that are verifiable. In the literature, numerous techniques have been proposed to make mix nets verifiable. Some of them have also been employed for securing real political elections. With the looming possibility of quantum computers and their threat to cryptosystems based on classical hardness assumptions, there is significant pressure to migrate mix nets to post-quantum alternatives. At present, no verifiable and practical post-quantum mix net with external auditing is available as a drop-in replacement of existing constructions. In this paper, we give the first such construction. We propose a verifiable decryption mix net which solely employs practical lattice-based primitives. We formally prove that our mix net provides a high level of verifiability, and even accountability which guarantees that misbehaving mix servers can also be identified. Verification is executed by a (temporarily trusted) public auditor whose role can easily be distributed. To demonstrate practicality for real-world systems, we provide detailed performance benchmarks on our stand-alone implementation based only on the most conservative lattice hardness assumptions.

The COVID-19 pandemic has posed a unique challenge for the world to find solutions, ranging from vaccines to ICT solutions to slow down the virus spreading. Due to the highly contagious nature of the virus, social distancing is one fundamental measure which has already adopted by many countries. At the technical level, this prioritises contact tracing solutions, which can alert the users who have been in close contact with the infected persons and meanwhile allow heath authorities to take proper actions. In this paper, we examine several existing privacy-aware contact tracing solutions and analyse their (dis)advantages. At the end, we describe several major observations and outline an interdisciplinary research agenda towards more comprehensive and effective privacy-aware contact tracing solutions.

Secure enclave architectures have become prevalent in modern CPUs and enclaves provide a flexible way to implement various hardware-assisted security services. But special-purpose security chips can still have advantages. Interestingly, dedicated security chips can also assist enclaves and improve their security.

Among numerous applications, besides cryptocurrencies, the Blockchain offers inherent properties beneficial for the management of supply chains, where data is shared between trusted and untrusted parties. Electronics supply chain serves as a prime example of such chains, where one of the major players, i.e., a foundry, can be untrusted. Hardware obfuscation techniques, namely logic locking, and IC camouflaging have been developed to mislead an adversary aiming at reverse- engineering and Intellectual Property (IP) piracy. However, virtually all existing hardware obfuscation schemes developed over the last decade have been shown to be vulnerable to various attacks. The success of these attacks has been relying on either a lack of thorough, cryptographically-secure obfuscation schemes or an incorrect assumption widely made, i.e., the existence of an ideal tamper- and read-proof memory to store the key. To overcome these shortcomings, this paper proposes a novel, Blockchain-enabled, cryptographically-secure hardware obfuscation schemes being compatible with current circuit synthesis and fabrication tools. In this regard, rather than solely monitoring the supply chain via the Blockchain, the security of the obfuscation is guaranteed by Proof-of-Stack Blockchain protocols and witness encryption schemes. Furthermore, with the help of our construction, we can realize one-time and pay-per-use hardware, where a user can use the electronic circuit for a limited amount of time.

Vehicular communication (V2X) technologies allow vehicles to exchange information about the road conditions and their own status, and thereby enhance transportation safety and efficiency. For broader deployment, however, such technologies are expected to address security and privacy concerns, preventing abuse by users and by the system's entities. In particular, the system is expected to enable the revocation of malicious vehicles, e.g., in case they send invalid information to their peers or to the roadside infrastructure; it should also prevent the system from being misused for tracking honest vehicles.Both features are enabled by Vehicular Public Key Infrastructure (VPKI) solutions such as Security Credential Management Systems (SCMS), one of the leading candidates for protecting V2X communication in the United States. Unfortunately, though, SCMS's original revocation mechanism can lead to large Certification Revocation Lists (CRLs), which in turn impacts the bandwidth usage and processing overhead of the system. In this article, we propose a novel design called Activation Codes for Pseudonym Certificates (ACPC), which can be integrated into SCMS to address this issue. Our proposal is based on activation codes, short bitstrings without which certificates previously issued to a vehicle cannot be used by the latter, which are periodically distributed to non-revoked vehicles using an efficient broadcast mechanism. As a result, the identifiers of the corresponding certificates do no need to remain on the CRL for a long time, reducing the CRLs' size and streamlining their distribution and verification of any vehicle's revocation status. Besides describing ACPC in detail, we also compare it to similar-purpose solutions such as Issue First Activate Later (IFAL) and Binary Hash Tree based Certificate Access Management (BCAM).This analysis shows that our proposal not only brings security improvements (e.g., in terms of resilience against colluding system authorities), but also leads to processing and bandwidth overheads that are orders of magnitude smaller than those observed in the state of the art.

Currently several projects (including DP-3T, east and west coast PACT, Covid watch) aim at designing and implementing protocols for privacy preserving automated contact tracing to help fight the current pandemic. Those proposal are very similar, and in their most basic from basically propose an app for mobile phones which broadcasts frequently changing pseudorandom identifiers via (low energy) Bluetooth, and at the same time, the app stores IDs broadcast by phones in its proximity. Only if a user is tested positive, their IDs of the last 14 days are published so other users can check if they have stored them locally and thus were close to an infected person. Vaudenay [eprint 2020/399] observes that this basic scheme (he considers the DP-3T proposal) succumbs to relay and even replay attacks, and proposes more complex \emph{interactive} schemes which prevent those attacks without giving up too many privacy aspects. Unfortunately interaction is problematic for this application for efficiency and security reasons. In this note propose a simple \emph{non-interactive} variant of the basic protocol that \begin{itemize} \item (security) Provably prevents replay and relay attacks. \item (privacy) The data of all parties (even jointly) reveals no information on the location or time where encounters happened. \item (efficiency) The broadcasted message can fit into 128 bits and uses simple crypto (commitments and secret key authentication). \end{itemize} Towards this end we introduce the concept of ``delayed authentication", which basically is a message authentication code where verification can be done in two steps, where the first doesn't require the key, and the second doesn't require the message.

We design a consecution of protocols which allows organizations to have secure strong access control of their users to their desktop machines based on biometry. It provides both strong secure authentication and privacy. Moreover, our mechanism allows the system admins to grant a various level of access to their end-users by fine tuning access control policy. Our system implements privacy-by-design. It separates biometric data from identity information. It is practical: we fully implemented our protocols as a proof of concept for a hospital. We use a 3D fingervein scanner to capture the biometric data of the user on a Raspberry Pi. For the biometry part, we developed an optimal way to aggregate scores using sequential distinguishers. It trades desired FAR and FRR against an average number of biometric captures.

Recent years have seen an increased interest towards strong security primitives for encrypted databases (such as oblivious protocols), that hide the access patterns of query execution, and reveal only the volume of results. However, recent work has shown that even volume leakage can enable the reconstruction of entire columns in the database. Yet, existing attacks rely on a set of assumptions that are unrealistic in practice: for example, they (i) require a large number of queries to be issued by the user, or (ii) assume certain distributions on the queries or underlying data (e.g., that the queries are distributed uniformly at random, or that the database does not contain missing values). In this work, we present new attacks for recovering the content of individual user queries, assuming no leakage from the system except the number of results and avoiding the limiting assumptions above. Unlike prior attacks, our attacks require only a single query to be issued by the user for recovering the keyword. Furthermore, our attacks make no assumptions about the distribution of issued queries or the underlying data. Instead, our key insight is to exploit the behavior of real-world applications. We start by surveying 11 applications to identify two key characteristics that can be exploited by attackers -- (i) file injection, and (ii) automatic query replay. We present attacks that leverage these two properties in concert with volume leakage, independent of the details of any encrypted database system. Subsequently, we perform an attack on the real Gmail web client by simulating a server-side adversary. Our attack on Gmail completes within a matter of minutes, demonstrating the feasibility of our techniques. We also present three ancillary attacks for situations when certain mitigation strategies are employed.

We facilitate trusted cross-blockchain state proofs by implementing a chain-relay that validates block headers from proof-of-work blockchains. While current approaches require proof sizes linear to the amount of blocks the state was built on, trusted intermediaries, or economic assumptions, we propose the utilization of off-chain computations through zkSNARKs to provide a cryptographically secure and highly scalable sidechain mechanism. Multiple block headers are included in batches and verified off-chain, while preserving light client support. Only the validity of the off-chain computation is verified on-chain, creating a sidechain mechanism that requires constant verification costs and releases the target ledger from processing and storing every single block header of the source blockchain. We provide a prototypical implementation that facilitates the verification of 504 Bitcoin headers in a single proof on Ethereum using the ZoKrates framework. Hereby, the verification costs are reduced by a factor of 187 compared to current approaches such as BTC Relay.

In this work we develop a rewarding framework that can be used as a building block in crowd-sensing applications. Although a core requirement of such systems is user engagement, people may be reluctant to participate as sensitive information about them may be leaked or inferred from submitted data. Thus monetary incentives could help attract a large number of participants, thereby increasing not only the amount but also the quality of sensed data. Our first contribution in this work is to ensure that users can submit data and obtain Bitcoin payments in a privacy-preserving manner, preventing curious providers from linking the data or the payments back to the user. At the same time, we thwart malicious user behavior such as double-redeeming attempts where a user tries to obtain rewards for multiple submissions of the same data. More importantly, we ensure the fairness of the exchange in a completely trustless manner; by relying on the Blockchain, we eliminate the trust placed on third parties in traditional fair exchange protocols. Finally, our system is highly efficient as most of the protocol steps do not utilize the Blockchain network. When they do, we only rely on simple Bitcoin transactions as opposed to prior works that are based on the use of highly complex smart contracts.

To help fighting the COVID-19 pandemic, the Pan-European Privacy-Preserving Proximity Tracing (PEPP-PT) project proposed a Decentralized Privacy-Preserving Proximity Tracing (DP3T) system. This helps tracking the spread of SARS-CoV-2 virus while keeping the privacy of individuals safe. In this report, we analyze the security and the privacy protection of DP3T. Without questioning how effective it could be against the pandemic, we show that it may introduce severe risks to society. Furthermore, we argue that some privacy protection measurements by DP3T may have the opposite affect of what they were intended to. Specifically, sick and reported people may be deanonymized, private encounters may be revealed, and people may be coerced to reveal the private data they collect.

Vehicular communication (V2X) technologies are expected to be common in the future, providing better transportation safety and efficiency. However, their large-scale deployment requires addressing some challenges. In particular, to prevent abuse by drivers and by the system itself, V2X architectures must: (1) ensure the authenticity of messages, which is usually accomplished by means of digital certification; and (2) preserve the privacy of honest users, so owners of non-revoked certificates cannot be easily identified or tracked by eavesdroppers. A promising solution for managing V2X-oriented certificates in an efficient manner is the Security Credential Management System (SCMS), which is among the main candidates for standardization in the United States. In this paper, aiming to enhance and address issues in the SCMS architecture, we provide three main contributions. First, we describe and fix two birthday attacks against SCMS's certificate revocation process, thus preventing the system's security degradation with the number of issued and revoked certificates. In addition, we describe a mechanism for improving the flexibility of revocation, allowing certificates and their owner's privacy to be temporarily revoked in an efficient manner; this functionality is useful, for example, in case of vehicle theft or kidnapping. Finally, we propose a method that simplifies SCMS's system architecture, removing the need for the so-called Linkage Authorities (LAs); this not only results in cost reductions for SCMS's implementation, but also improves its security and privacy due to the removal of one potential point of failure/collusion.

We advance the study of secure stream-based channels (Fischlin et al., CRYPTO ’15) by considering the multiplexing of many data streams over a single channel, an essential feature of real world protocols such as TLS. Our treatment adopts the definitional perspective of Rogaway and Stegers (CSF ’09), which offers an elegant way to reason about what standardizing documents actually provide: a partial specification of a protocol that admits a collection of compliant, fully realized implementations. We formalize partially specified channels as the component algorithms of two parties communicating over a channel. Each algorithm has an oracle that provides specification details; the algorithms abstract the things that must be explicitly specified, while the oracle abstracts the things that need not be. Our security notions, which capture a variety of privacy and integrity goals, allow the adversary to respond to these oracle queries; security relative to these notions implies that the channel withstands attacks in the presence of worst-case (i.e., adversarial) realizations of the specification details. We apply this framework to a formal treatment of the TLS 1.3 record and, in doing so, show that its security hinges crucially upon details left unspecified by the standard.

Fault attack is a class of active implementation based attacks which introduces controlled perturbations in the normal operation of a system to produce faulty outcomes. In case of ciphers, these faulty outcomes can lead to leakage of secret information, such as the secret key. The effectiveness and practicality of fault attacks largely depend on the underlying fault model and the type of fault induced. In this paper, we analyse the drawbacks of persistent fault model in case of error correction code (ECC) enabled systems. We further propose a novel fault attack called Intermittent Fault Attack which is well suited for ECC-enabled DRAM modules. We demonstrate the practicality of our attack model by inducing single bit faults using pinpointed Rowhammer technique in S-Boxes of block ciphers in an ECC protected system.

We propose BSC, a Bitcoin Smart Contract implementation. It integrates the functionality of smart contracts into the Bitcoin system, giving developers the ability to build decentralized applications on Bitcoin. BSC will require a new hard fork, on which Bitcoin holders can use their existing funds directly. BSC combines the unlimited creative space of smart contracts and the vast network effect of Bitcoin, which will bring even more possibilities to the cryptocurrency world.

Blockchains have the potential to revolutionize markets and services. However, they currently exhibit high latencies and fail to handle transaction loads comparable to those managed by traditional financial systems. Layer-two protocols, built on top of layer-one blockchains, avoid disseminating every transaction to the whole network by exchanging authenticated transactions off-chain. Instead, they utilize the expensive and low-rate blockchain only as a recourse for disputes. The promise of layer-two protocols is to complete off-chain transactions in sub-seconds rather than minutes or hours while retaining asset security, reducing fees and allowing blockchains to scale. We systematize the evolution of layer-two protocols over the period from the inception of cryptocurrencies in 2009 until today, structuring the multifaceted body of research on layer-two transactions. Categorizing the research into payment and state channels, commit-chains and protocols for refereed delegation, we provide a comparison of the protocols and their properties. We provide a systematization of the associated synchronization and routing protocols along with their privacy and security aspects. This Systematization of Knowledge (SoK) clears the layer-two fog, highlights the potential of layer-two solutions and identifies their unsolved challenges, indicating propitious avenues of future work.

Data minimization has become a paradigm to address privacy concerns when collecting and storing personal data. In this paper we present two new approaches, RSTxFM and RRTxFM, to estimate the cardinality of a dataset while ensuring differential privacy. We argue that privacy-preserving cardinality estimators are able to realize strong privacy requirements. Both approaches are based on a probabilistic counting algorithm which has a logarithmic space complexity. We combine this with a randomization technique to provide differential privacy. In our analysis, we detail the privacy and utility guarantees and expose the impact of the various parameters. Moreover, we discuss workforce analytics as application area where strong privacy is paramount.

This paper explores the design space of secure communication in ultra-low-energy IoT devices based on Micro-Controller Units (MCUs). It tries to identify, evaluate and compare security-related design choices in a Commercial-Off-The-Shelf (COTS) embedded IoT system which contribute in the energy consumption. We conduct a study over a large group of software-implemented crypto algorithms: symmetric, stream, hash, AEAD, MAC, digital signature and key exchange. A comprehensive report of the targeted optimization attributes (memory, performance and specifically energy) will be presented from over 450 experiments and 170 different crypto source codes. The paper also briefly explores a few system-related choices which can affect the energy consumption of secure communication, namely: architecture choice, communication bandwidth, signal strength and processor frequency. In the end, the paper gives an overview on the obtained results and the contribution of all. Finally it shows, in a case study, how the results could be utilized to have a secure communication in an exemplary IoT device. This paper gives IoT designers an insight on the ultra-low-energy security, helps them to choose appropriate cryptographic algorithms, reduce trial-and-error of alternatives, save effort and hence cut the design costs.

Recently, a logic locking approach termed `CAS-Lock' was proposed to simultaneously counter Boolean satisfiability (SAT) and bypass attacks. The technique modifies the AND/OR tree structure in Anti-SAT to achieve non-trivial output corruptibility while maintaining resistance to both SAT and bypass attacks. An attack against CAS-Lock (dubbed `CAS-Unlock') was also recently proposed on a naive implementation of CAS-Lock. It relies on setting key values to all 1's or 0's to break CAS-Lock. In this short paper, we evaluate this attack's ineffectiveness and describe a misinterpretation of CAS-Lock's implementation.

The $k$-nearest neighbors ($k$NN) classifier predicts a class of a query, $q$, by taking the majority class of its $k$ neighbors in an existing (already classified) database, $S$. In secure $k$NN, $q$ and $S$ are owned by two different parties and $q$ is classified without sharing data. In this work we present a classifier based on $k$NN, that is more efficient to implement with homomorphic encryption (HE). The efficiency of our classifier comes from a relaxation we make to consider $\kappa$ nearest neighbors for $\kappa\approx k$ with probability that increases as the statistical distance between Gaussian and the distribution of the distances from $q$ to $S$ decreases. We call our classifier $k$-ish Nearest Neighbors ($k$-ish NN). For the implementation we introduce {\em double-blinded coin-toss} where the bias and output of the toss are encrypted. We use it to approximate the average and variance of the distances from $q$ to $S$ in a scalable circuit whose depth is independent of $|S|$. We believe these to be of independent interest. We implemented our classifier in an open source library based on HElib and tested it on a breast tumor database. Our classifier has accuracy and running time comparable to current state of the art (non-HE) MPC solution that have better running time but worse communication complexity. It also has communication complexity similar to naive HE implementation that have worse running time.

While the digital technology spreads through the society, reliable personal authentication is becoming an urgent issue. As shown in digital taxation (e-Tax) and blockchain, etc., high reliable link between the private key of a public key and the owner who has it in card or smartphone etc. is required. This paper proposes 3 layer public key cryptosystem in which Individual Number (a.k.a. "My Number") and STR (Short Tandem Repeat) as personal identification data installed. "Individual Number" is a national identification number issued by government, like social security number in USA. STR is a kind of DNA data which does not contain any subtle personal information such as inherited character and has very accurate personal identification. The proposed system satisfies requirements of integrity, soundness and zero knowledge characteristics which analog biometrics such as face authentications cannot provide.

This work considers the security of systems that process encrypted multi-dimensional range queries with only access pattern leakage. Recent work of Kellaris et al. (CCS 2016) showed that in one dimension, an adversary could use the access patterns of several uniformly random range queries to reconstruct a plaintext column of numbers “up to reflection.” We extend this attack to two dimensions and find that the situation is much more complicated: Information theoretically it is complex to describe even what is possible to recover for the adversary in general. We provide a classification of these limits under certain technical conditions. We also give a faster algorithm that works for “dense” databases that contain at least one record for each possible value. Finally we explore the implications for our classification with real data sets.

The $k$-Nearest Neighbor Search ($k$-NNS) is the backbone of several cloud-based services such as recommender systems, face recognition, and database search on text and images. In these services, the client sends the query to the cloud server and receives the response in which case the query and response are revealed to the service provider. Such data disclosures are unacceptable in several scenarios due to the sensitivity of data and/or privacy laws. In this paper, we introduce SANNS, a system for secure $k$-NNS that keeps client's query and the search result confidential. SANNS comprises two protocols: an optimized linear scan and a protocol based on a novel sublinear time clustering-based algorithm. We prove the security of both protocols in the standard semi-honest model. The protocols are built upon several state-of-the-art cryptographic primitives such as lattice-based additively homomorphic encryption, distributed oblivious RAM, and garbled circuits. We provide several contributions to each of these primitives which are applicable to other secure computation tasks. Both of our protocols rely on a new circuit for the approximate top-$k$ selection from $n$ numbers that is built from $O(n + k^2)$ comparators. We have implemented our proposed system and performed extensive experimental results on four datasets in two different computation environments, demonstrating more than $18-31\times$ faster response time compared to optimally implemented protocols from the prior work. Moreover, SANNS is the first work that scales to the database of 10 million entries, pushing the limit by more than two orders of magnitude.

Access and search pattern leakage have been shown to be detrimental to the security of encrypted databases that allow for range queries, as shown by an extensive body of work on efficient attacks that reconstruct one-dimensional databases. We are the first to go beyond one dimension, exploring the threat of access and search pattern leakage in two dimensions. First, we unveil an intrinsic limitation of reconstruction attacks by showing that there can be an exponential number of distinct databases that produce the same access and search pattern leakage. Next, we present attacks that reconstruct (1) the horizontal and vertical order of the points from the access pattern leakage, and (2) the coordinates of the points from the access and search pattern leakage. Our algorithms run in polynomial time and return a linear-size encoding of all databases consistent with the given leakage profile.

Privacy and machine learning are difficult to coexist due to their nature: parivacy should be kept from others while machine learning requires large amount of data. Among several possible solutions to this problem, Fully Homomorphic Encryption has been a center of intensive researches in this field. FHE enables linear operations of ciphertext. To take advantage of this property, many protocols to achieve statistical operaions have been proposed. On the other hand, many of them are impractical. Some of the approaches introduce cryptosystems that are not familiar. Moreover, most of their protocols are approximation which might sensitively depend on our choice of parameters. In this paper, we propose fast, simple, and exact privacy-preserving linear equation solver using FHE. Our two-party protocol is secure against at least semi-honest model, and we can exactly calculate the model even without the bootstrapping.

To maintain the availability of industrial control systems (ICS), it is important to robustly detect malware infection that spreads within the ICS network. In ICS, a host often communicates with the determined hosts; for instance, a supervisory control host observes and controls the same devices routinely via the network. Therefore, a communication request to the unused internet protocol (IP) address space, i.e. darknet, in the ICS network is likely to be caused by malware in the compromised host in the network. That is, darknet monitoring may enable us to detect malware that tries to spread indiscriminately within the network. On the other hand, advanced malware, such as malware determining target hosts of infection with reference to host lists in the networks, infects the confined hosts in the networks, and consequently evades detection by security sensors or honeypots. In this paper, we propose novel deception techniques that lure such malware to our sensor, by embedding the sensor information continuously in the lists of hosts in the ICS networks. In addition, the feasibility of the proposed deception techniques is shown through our simplified implementation by using actual malware samples: WannaCry and Conficker.

Viktoria hash is a compression function that generates a set of 512 bits from an arbitrary size input (limit of 2^480-1 bytes). This hash function contains some internal routines clearly inspired by AES and RC4 symmetric algorithms [14]. The new paradigm presents two major innovations: a fast preprocessing that initiates an internal state of 256!^2 permutations and a post-processing that guarantees a minimum number of executed rounds of 2^13. The pre-processing allows to differentiate very similar messages in the first runs of the algorithm. In the post-processing we have a safety barrier provided by a large number of rounds through a different structure of the main processing. The Viktoria algorithm seems to inaugurate a new design model in the construction of robust hash functions for some reasons, among them we highlight: the customization of the internal state according to each message, the elegance and efficiency of its main function and also a supposed high margin of safety provided by its post-processing function. Viktoria hash can also process bit oriented messages (whose last byte size is not complete) and generate larger hashes (1024, 1536, 2048 or larger) always as multiples of 512.

Authentication and identification methods based on human fingerprints are ubiquitous in several systems ranging from government organizations to consumer products. The performance and reliability of such systems directly rely on the volume of data on which they have been verified. Unfortunately, a large volume of fingerprint databases is not publicly available due to many privacy and security concerns. In this paper, we introduce a new approach to automatically generate high-fidelity synthetic fingerprints at scale. Our approach relies on (i) Generative Adversarial Networks to estimate the probability distribution of human fingerprints and (ii) Super- Resolution methods to synthesize fine-grained textures. We rigorously test our system and show that our methodology is the first to generate fingerprints that are computationally indistinguishable from real ones, a task that prior art could not accomplish.

Neural networks have become increasingly prevalent in many real-world applications including security-critical ones. Due to the high hardware requirement and time consumption to train high-performance neural network models, users often outsource training to a machine-learning-as-a-service (MLaaS) provider. This puts the integrity of the trained model at risk. In 2017, Liu et. al. found that, by mixing the training data with a few malicious samples of a certain trigger pattern, hidden functionality can be embedded in the trained network which can be evoked by the trigger pattern. We refer to this kind of hidden malicious functionality as neural Trojans. In this paper, we survey a myriad of neural Trojan attack and defense techniques that have been proposed over the last few years. In a neural Trojan insertion attack, the attacker can be the MLaaS provider itself or a third party capable of adding or tampering with training data. In most research on attacks, the attacker selects the Trojan's functionality and a set of input patterns that will trigger the Trojan. Training data poisoning is the most common way to make the neural network acquire Trojan functionality. Trojan embedding methods that modify the training algorithm or directly interfere with the neural network's execution at the binary level have also been studied. Defense techniques include detecting neural Trojans in the model and/or Trojan trigger patterns, erasing the Trojan's functionality from the neural network model, and bypassing the Trojan. It was also shown that carefully crafted neural Trojans can be used to mitigate other types of attacks. We systematize the above attack and defense approaches in this paper.

We study the communication complexity of unconditionally secure MPC with guaranteed output delivery over point-to-point channels for corruption threshold t < n/2, assuming the existence of a public broadcast channel. We ask the question: “is it possible to construct MPC in this setting s.t. the communication complexity per multiplication gate is linear in the number of parties?” While a number of works have focused on reducing the communication complexity in this setting, the answer to the above question has remained elusive until now. We also focus on the concrete communication complexity of evaluating each multiplication gate. We resolve the above question in the affirmative by providing an MPC with communication complexity O(Cn\phi) bits (ignoring fixed terms which are independent of the circuit) where \phi is the length of an element in the field, C is the size of the (arithmetic) circuit, n is the number of parties. This is the first construction where the asymptotic communication complexity matches the best-known semi-honest protocol. This represents a strict improvement over the previously best-known communication complexity of O(C(n\phi+\kappa)+D_Mn^2\kappa) bits, where \kappa is the security parameter and D_M is the multiplicative depth of the circuit. Furthermore, the concrete communication complexity per multiplication gate is 5.5 field elements per party in the best case and 7.5 field elements in the worst case when one or more corrupted parties have been identified. This also roughly matches the best-known semi-honest protocol, which requires 5.5 field elements per gate.

The abilities of smart contracts today are confined to reading from their own state. It is useful for a smart contract to be able to react to events and read the state of other smart contracts. In this paper, we devise a mechanism by which a derivative smart contract can read data, observe the state evolution, and react to events that take place in one or more underlying smart contracts of its choice. Our mechanism works even if the underlying smart contract is not designed to operate with the derivative smart contract. Like in traditional finance, derivatives derive their value (and more generally state) through potentially complex dependencies. We show how derivative smart contracts can be deployed in practice on the Ethereum blockchain without any forks or additional assumptions. We leverage any NIPoPoWs mechanism (such as FlyClient or superblocks) to obtain succinct proofs for arbitrary events, making proving them inexpensive for users. The latter construction is of particular interest, as it forms the first introspective SPV client: an SPV client for Ethereum in Ethereum. Last, we describe applications of smart contract derivatives which were not possible prior to our work, in particular the ability to create decentralized insurance smart contracts which insure an underlying on-chain security such as an ICO, as well as futures and options.

McCorry et al. (Financial Cryptography 2017) presented the first implementation of a decentralized self-tallying voting protocol on Ethereum. However, their implementation did not scale beyond 40 voters since all the computations were performed on the smart contract. In this paper, we tackle this problem by delegating the bulk computations to an off-chain untrusted administrator in a verifiable manner. Specifically, the administrator tallies the votes off-chain and publishes a Merkle tree that encodes the tallying computation trace. Then, the administrator submits the Merkle tree root and the tally result to the smart contract. Subsequently, the smart contract transits to an intermediate phase where at least a single honest voter can contend the administrator's claimed result if it was not computed correctly. Then, in the worst case, the smart contract verifies the dispute at the cost of an elliptic curve point addition and scalar multiplication, and two Merkle proofs of membership which are logarithmic in the number of voters. This allows our protocol to achieve higher scalability without sacrificing the public verifiability or voters' privacy. To assess our protocol, we implemented an open-source prototype on Ethereum and carried out multiple experiments for different numbers of voters. The results of our implementation confirm the scalability and efficiency of our proposed solution which does not exceed the current block gas limit for any practical number of voters.

Blockchains such as the bitcoin blockchain depend on reaching a global consensus on the distributed ledger; therefore, they suffer from well know scalability problems. This paper proposes an algorithm that avoids double-spending in the short term with just O(&#8730;n) messages instead of O(n); each node receiving money off-chain performs the due diligence of consulting k&#8730;n random nodes to check if any of them is aware of double-spending. Two nodes receiving double-spent money will in this way consult at least one common node with very high probability, due to the ‘birthday paradox’, and any common honest node consulted will detect the fraud. Since the velocity of money in the real world has coins circulating through at most a few wallets per day, the size of the due diligence communication is small in the short term. This `k-root-n’ algorithm is suitable for an environment with synchronous or asynchronous (but with fairly low latency) communication and with Byzantine faults. The presented k-root-n algorithm should be practical to avoid double-spending with arbitrarily high probability, while feasibly coping with the throughput of all world commerce. It is resistant to Sybil attacks even beyond 50% of nodes. In the long term, the k-root-n algorithm is less efficient. Therefore, it should preferably be used as a complement, and not a replacement, to a global distributed ledger technology.

The survey deals with elliptic curves which are implemented in the OpenSSL 1.1.1d software library. The objective of this work is to highlight the elliptic curves which comply with the Russian national digital signature standard, namely the GOST R 34.10--2012. For this reason the paper focuses on the OpenSSL elliptic curves over a finite field of a prime order and provides the results of testing those curves for compliance with the GOST R 34.10--2012 requirements. Two cases are observed. The first case covers a complete set of restrictions imposed on elliptic curves parameters, whereas the second one differs in that a restriction on a bit length of a number of points on the curve is omitted. For both cases the paper presents tables which list the curves tested along with corresponding match marks. In order to conduct tests the Wolfram Mathematica computing system was employed, and the Wolfram language source code is given in the appendices. Note, that the paper does not address to a rationale of the requirements of the standard nor does it focus on the parameters generation issues.

The absence of deployed vehicular communication systems, which prevents the advanced driving assistance systems (ADASs) and autopilots of semi/fully autonomous cars to validate their virtual perception regarding the physical environment surrounding the car with a third party, has been exploited in various attacks suggested by researchers. Since the application of these attacks comes with a cost (exposure of the attacker’s identity), the delicate exposure vs. application balance has held, and attacks of this kind have not yet been encountered in the wild. In this paper, we investigate a new perceptual challenge that causes the ADASs and autopilots of semi/fully autonomous to consider depthless objects (phantoms) as real. We show how attackers can exploit this perceptual challenge to apply phantom attacks and change the abovementioned balance, without the need to physically approach the attack scene, by projecting a phantom via a drone equipped with a portable projector or by presenting a phantom on a hacked digital billboard that faces the Internet and is located near roads. We show that the car industry has not considered this type of attack by demonstrating the attack on today’s most advanced ADAS and autopilot technologies: Mobileye 630 PRO and the Tesla Model X, HW 2.5; our experiments show that when presented with various phantoms, a car’s ADAS or autopilot considers the phantoms as real objects, causing these systems to trigger the brakes, steer into the lane of oncoming traffic, and issue notifications about fake road signs. In order to mitigate this attack, we present a model that analyzes a detected object’s context, surface, and reflected light, which is capable of detecting phantoms with 0.99 AUC. Finally, we explain why the deployment of vehicular communication systems might reduce attackers’ opportunities to apply phantom attacks but won’t eliminate them.

Primality testing is a basic cryptographic task. But developers today are faced with complex APIs for primality testing, along with documentation that fails to clearly state the reliability of the tests being performed. This leads to the APIs being incorrectly used in practice, with potentially disastrous consequences. In an effort to overcome this, we present a primality test having a simplest-possible API: the test accepts a number to be tested and returns a Boolean indicating whether the input was composite or probably prime. For all inputs, the output is guaranteed to be correct with probability at least $1 - 2^{128}$. The test is performant: on random, odd, 1024-bit inputs, it is faster than the default test used in OpenSSL by 17\%. We investigate the impact of our new test on the cost of random prime generation, a key use case for primality testing. The OpenSSL developers have adopted our suggestions in full; our new API and primality test are scheduled for release in OpenSSL 3.0.

Centralized pools and renting of mining power are considered as sources of possible censorship threats and even 51% attacks for decentralized cryptocurrencies. Non-outsourceable Proof-of-Work schemes have been proposed to tackle these issues. However, tenets in the folklore say that such schemes could potentially be bypassed by using escrow mechanisms. In this work, we propose a concrete example of such a mechanism which is using collateralized smart contracts. Our approach allows miners to bypass non-outsourceable Proof-of-Work schemes if the underlying blockchain platform supports smart contracts in a sufficiently advanced language. In particular, the language should allow access to the PoW solution. At a high level, our approach requires the miner to lock collateral covering the reward amount and protected by a smart contract that acts as an escrow. The smart contract has logic that allows the pool to collect the collateral as soon as the miner collects any block reward. We propose two variants of the approach depending on when the collateral is bound to the block solution. Using this, we show how to bypass previously proposed non-outsourceable Proof-of-Work schemes (with the notable exception for strong non-outsourceable schemes) and show how to build mining pools for such schemes.

Dynamic and partial reconfiguration together with hardware parallelism make FPGAs attractive as virtualized accelerators. However, recently it has been shown that multi-tenant FPGAs are vulnerable to remote side-channel attacks (SCA) from malicious users, allowing them to extract secret keys without a logical connection to the victim core. Typical mitigations against such attacks are hiding and masking schemes, to increase attackers’ efforts in terms of side-channel measurements. However, they require significant efforts and tailoring for a specific algorithm, hardware implementation and mapping. In this paper, we show a hiding countermeasure against voltage-based SCA that can be integrated into any implementation, without requiring modifications or tailoring to the protected module. We place a properly mapped Active Fence of ring oscillators between victim and attacker circuit, enabled as a feedback of an FPGA-based sensor, leading to reduced side-channel leakage. Our experimental results based on a Lattice ECP5 FPGA and an AES-128 module show that two orders of magnitude more traces are needed for a successful key recovery, while no modifications to the underlying cryptographic module are necessary.

We design an efficient method for sampling a large batch of $d$ independent coins with a given bias $p \in [0,1]$. The folklore secure computation method for doing so requires $O(\lambda + \log d)$ communication and computation per coin to achieve total statistical difference $2^{-\lambda}$. We present an exponential improvement over the folklore method that uses just $O(\log(\lambda+\log d))$ gates per coin when sampling $d$ coins with total statistical difference $2^{-\lambda}$. We present a variant of our work that also concretely beats the folklore method for $\lambda \geq 60$ which are parameters that are often used in practice. Our new technique relies on using specially designed oblivious data structures to achieve biased coin samples that takean expected $2$ random bits to sample. Using our new sampling technique, we present an implementation of the differentially private report-noisy-max mechanism (a more practical implementation of the celebrated exponential mechanism) as a secure multi-party computation. Our benchmarks show that one can run this mechanism on a domain of size $d=2^{12}$ in 6 seconds and up to $d=2^{19}$ in 14 minutes. As far as we know, this is the first complete distributed implementation of either of these mechanisms.

The Shell sort algorithm is one of the most practically effective sorting algorithms. However, it is difficult to execute this algorithm with its intended running time complexity on data encrypted using fully homomorphic encryption (FHE), because the insertion sort in Shell sort has to be performed by considering the worst-case input data. In this paper, in order for the sorting algorithm to be used on FHE data, we modify the Shell sort with an additional parameter $\alpha$ and a gap sequence of powers of two. The modified Shell sort is found to have the trade-off between the running time complexity of $O(n^{3/2}\sqrt{\alpha+\log\log n})$ and the sorting failure probability of $2^{-\alpha}$. Its running time complexity is close to the intended running time complexity of $O(n^{3/2})$ and the sorting failure probability can be made very low with slightly increased running time. Further, the optimal window length of the modified Shell sort is also derived via convex optimization. The proposed analysis of the modified Shell sort is numerically confirmed by using randomly generated arrays. Further, the performance of the modified Shell sort is numerically compared with the case of Ciura's optimal gap sequence and the case of the optimal window length obtained through the convex optimization.

A symmetric searchable encryption (SSE) scheme allows a client (data owner) to search on encrypted data outsourced to an untrusted cloud server. The search may either be a single keyword search or a complex query search like conjunctive or Boolean keyword search. Information leakage is quite high for dynamic SSE, where data might be updated. It has been proven that to avoid this information leakage an SSE scheme with dynamic data must be forward private. A dynamic SSE scheme is said to be forward private, if adding a keyword-document pair does not reveal any information about the previous search result with that keyword. In SSE setting, the data owner has very low computation and storage power. In this setting, though some schemes achieve forward privacy with honest-but-curious cloud, it becomes difficult to achieve forward privacy when the server is malicious, meaning that it can alter the data. Verifiable dynamic SSE requires the server to give a proof of the result of the search query. The data owner can verify this proof efficiently. In this paper, we have proposed a generic publicly verifiable dynamic SSE (DSSE) scheme that makes any forward private DSSE scheme verifiable without losing forward privacy. The proposed scheme does not require any extra storage at owner-side and requires minimal computational cost as well for the owner. Moreover, we have compared our scheme with the existing results and show that our scheme is practical.

The world has seen an influx of connected devices through both smart devices and smart cities, paving the path forward for the Internet of Things (IoT). These emerging intelligent infrastructures and applications based on IoT can be beneficial to users only if essential private and secure features are assured. However, with constrained devices being the norm in IoT, security and privacy are often minimized. In this paper, we first categorize various existing privacy-enhancing technologies (PETs) and assessment of their suitability for privacy-requiring services within IoT. We also categorize potential privacy risks, threats, and leakages related to various IoT use cases. Furthermore, we propose a simple novel privacy-preserving framework based on a set of suitable privacy-enhancing technologies in order to maintain security and privacy within IoT services. Our study can serve as a baseline of privacy-by-design strategies applicable to IoT based services, with a particular focus on smart things, such as safety equipment.

Machinelearning(ML)istodaycommonlyemployedintheFinancialServicesSector(FSS) to create various models to predict a variety of conditions ranging from financial transactions fraud to outcomes of investments and also targeted marketing campaigns. The common ML technique used for the modeling is supervised learning using regression algorithms and usually involves large amounts of data that needs to be shared and prepared before the actual learning phase. Compliance with privacy laws and confidentiality regulations requires that most, if not all, of the data must be kept in a secure environment, usually in-house, and not outsourced to cloud or multi-tenant shared environments. This paper presents the results of a research collaboration between IBM Research and Banco Bradesco SA to investigate approaches to homomorphically secure a typical ML pipeline commonly employed in the FSS industry. We investigated and de-constructed a typical ML pipeline used by Banco Bradesco and applied Homo- morphic Encryption (HE) to two of the important ML tasks, namely the variable selection phase of the model generation task and the prediction task. Variable selection, which usually precedes the training phase, is very important when working with data sets for which no prior knowledge of the covariate set exists. Our work provides a way to define an initial covariate set for the training phase while preserving the privacy and confidentiality of the input data sets. Quality metrics, using real financial data, comprising quantitative, qualitative and categorical features, demonstrated that our HE based pipeline can yield results comparable to state of the art variable selection techniques and the performance results demonstrated that HE technology has reached the inflection point where it can be useful in batch processing in a financial business setting.

The active participation of external entities in the manufacturing flow has produced numerous hardware security issues in which piracy and overproduction are likely to be the most ubiquitous and expensive ones. The main approach to prevent unauthorized products from functioning is logic encryption that inserts key-controlled gates to the original circuit in a way that the valid behavior of the circuit only happens when the correct key is applied. The challenge for the security designer is to ensure neither the correct key nor the original circuit can be revealed by different analyses of the encrypted circuit. However, in state-of-the-art logic encryption works, a lot of performance is sold to guarantee security against powerful logic and structural attacks. This contradicts the primary reason of logic encryption that is to protect a precious design from being pirated and overproduced. In this paper, we propose a bilateral logic encryption platform that maintains high degree of security with small circuit modification. The robustness against exact and approximate attacks is also demonstrated.

This work presents Arcula, a new design for hierarchical deterministic wallets that brings identity-based addresses to the blockchain. Arcula is built on top of provably secure cryptographic primitives. It generates all its cryptographic secrets from a user-provided seed and enables the derivation of new public keys based on the identities of users, without requiring any secret information. Unlike other wallets, it achieves all these properties while being secure against privilege escalation. We formalize the security model of hierarchical deterministic wallets and prove that an attacker compromising an arbitrary number of users within an Arcula wallet cannot escalate his privileges and compromise users higher in the access hierarchy. Our design works out-of-the-box with any blockchain that enables the verification of signatures on arbitrary messages. We evaluate its usage in a real-world scenario on the Bitcoin Cash network.

Internet-of-Things enables interconnection of billions of devices, which perform autonomous operations and collect various types of data. These things, along with their generated huge amount of data, need to be handled efficiently and securely. Centralized solutions are not desired due to security concerns and scalability issue. In this paper, we propose HIBEChain, a hierarchical blockchain system that realizes scalable and accountable management of IoT devices and data. HIBEChain consists of multiple permissioned blockchains that form a hierarchical tree structure. To support the hierarchical structure of HIBEChain, we design a decentralized hierarchical identity-based signature (DHIBS) scheme, which enables IoT devices to use their identities as public keys. Consequently, HIBEChain achieves high scalability through parallel processing as blockchain sharding schemes, and it also implements accountability by use of identity-base keys. Identity-based keys not only make HIBEChain more user-friendly, they also allow private key recovery by validators when necessary. We provide detailed analysis of its security and performance, and implement HIBEChain based on Ethereum source code. Experiment results show that a 6-ary, (7,10)-threshold, 4-level HIBEChain can achieve 32,000 TPS, and it needs only 9 seconds to confirm a transaction.

Printed Electronics (PE) has a rapidly growing market, thus, the counterfeiting/overbuilding of PE components is anticipated to grow. The common solution for the counterfeiting is Physical Unclonable Functions (PUFs). In PUFs, a unique fingerprint is extracted from (irreproducible) process variations in the production and used in the authentication of valid components. Many commonly used PUFs are electrical PUFs by leveraging the impact of process variations on electrical properties of devices, circuits and chips. Hence, they add overhead to the production which results in additional costs. While such costs may be negligible for many application domains targeted by silicon-based VLSI technologies, they are detrimental to the ultra-low-cost PE applications. In this paper, we propose an optical PUF (iPUF) extracting a fingerprint from the optically visible variation of printed inks in the PE components. Since iPUF does not require any additional circuitry, the PUF production cost consists of merely acquisition, processing and saving an image of the circuit components, matching the requirements of ultra-low-cost margin applications of PE. To further decrease the storage costs for iPUF, we utilize image downscaling resulting in a compression rate of 484x, while still preserving the reliability and uniqueness of the fingerprints. The proposed fingerprint extraction methodology is applied to four datasets for evaluation. The results show that the process variation of the optical shapes of printed inks is suitable as an optical PUF to prevent counterfeiting in PE.

Most electronic voting systems today satisfy the basic requirements of privacy, unreusability, eligibility and fairness in a natural and rather straightforward way. However, receipt-freeness, incoercibility and universal verifiability are much harder to implement and in many cases they require a large amount of computation and communication overhead. In this work, we propose a blockchain-based voting system which achieves all the properties expected from secure elections without requiring too much from the voter. Coercion resistance and receipt-freeness are ensured by means of a randomizer token -- a tamper-resistance source of randomness which acts as a black box in constructing the ballot for the user. Universal verifiability is ensured by the append-only structure of the blockchain, thus minimizing the trust placed in election authorities. Additionally, the system has linear overhead when tallying the votes, hence it is scalable and practical for large scale elections.

Lattice-based cryptography offers quantum-resistant cryptosystems but there is not yet official recommendations to choose parameters with standard security levels. Some of these cryptosystems permit secure computations and aim at a wider audience than cryptographic community. We focus on one of them, a leveled homomorphic cryptosystem (LHE): Brakersi/Fan-Vercauteren's (BFV) one. The family of LHE cryptosystems needs to be well-instantiated not only to protect input and output ciphertexts and to perform efficiently computations, but also, for them, parametrization constrains the quantity of homomorphic computations that can be performed with guarantee of correctness. It demands to choose parameters accordingly. In addition, each implementation brings external constraints to optimize performance. All of this makes it tedious for the non-expert user to choose parameters. To solve this, we have developed CinguParam to help user to instantiate implementations of BFV in different libraries: Cingulata, FV-NFLlib and Microsoft SEAL. CinguParam permits to generate an up-to-date database of parameter sets in function of computation budget, security parameters and implementation choices. This tool includes a notion of budget to ensure correct homomorphic computations and the one of BKZ reduction cost model to grasp the gap from concrete security, nowadays. It makes use of the LWE-Estimator to obtain up-to-date security estimations. CinguParam permits to select automatically a suitable parameter set with Cingulata and it can be used to generate code snippets to set parameters with FV-NFLlib and Microsoft SEAL.

Metering is a critical process in large-scale distributed industrial plants, which enables multiple plants to collaborate to offer mutual services without outside interference. When distributed plants measure the data from a shared common source, e.g., flow metering in an oil pipeline, trustworthiness and immutability must be guaranteed among them. In this paper, we propose a hierarchical and scalable blockchain-based secure metering system, \textit{SMChain}, to provide strong security, trustworthy guarantee, and immutable services. {\em SMChain} adopts a two-layer blockchain structure, consisting of independent local blockchains stored at individual plants and one state blockchain stored in the cloud. To deal with the scalability issues within each plant, we propose a novel scalable Byzantine Fault Tolerance (BFT) consensus protocol based on \textit{(k, n)}-threshold signature scheme to deal with the Byzantine faults and reduce the intra-plant communication complexity from $O(n^2)$ to $O(n)$. For the state blockchain, we use a cloud-based service to synchronize and integrate the local blockchains into one state blockchain, which can further be distributed back to each plant.

Using a set of pairing product equations (PPEs) to verify the correctness of an untrusted set of pairing elements with respect to another set of trusted elements has numerous cryptographic applications. These include the design of basic and structure-preserving signature schemes, building oblivious transfer schemes from “blind” IBE, finding new verifiable random functions and keeping the IBE/ABE authority “accountable” to the user. A natural question to ask is: are all trusted-untrusted pairing element groups in the literature PPE testable? We provide original observations demonstrating that the answer is no, and moreover, it can be non-trivial to determine whether or not there exists a set of PPEs that can verify some pairing elements with respect to others. Many IBE schemes have PPE-testable private keys (with respect to the public parameters), while others, such as those based on dual-system encryption, provably do not. To aid those wishing to use PPE-based element verification in their cryptosystems, we devised rules to systematically search for a set of PPEs that can verify untrusted elements with respect to a set of trusted elements. We prove the correctness of each rule and combine them into a main searching algorithm for which we also prove correctness. We implemented this algorithm in a new software tool, called AutoPPE. Tested on over two dozen case studies, AutoPPE found a set of PPEs (on schemes where they exist) usually in just a matter of seconds. This work represents an important step towards the larger goal of improving the speed and accuracy of pairing-based cryptographic design via computer automation.

Secure distance measurement and therefore secure Time-of-Arrival (ToA) measurement is critical for applications such as contactless payments, passive-keyless entry and start systems, and navigation systems. This paper initiates the study of Message Time of Arrival Codes (MTACs) and their security. MTACs represent a core primitive in the construction of systems for secure ToA measurement. By surfacing MTACs in this way, we are able for the first time to formally define the security requirements of physical-layer measures that protect ToA measurement systems against attacks. Our viewpoint also enables us to provide a unified presentation of existing MTACs (such as those proposed in distance-bounding protocols and in a secure distance measurement standard) and to propose basic principles for protecting ToA measurement systems against attacks that remain unaddressed by existing mechanisms. We also use our perspective to systematically explore the tradeoffs between security and performance that apply to all signal modulation techniques enabling ToA measurements.

Mobile autonomous systems, robots, and cyber-physical systems rely on accurate positioning information. To conduct distance-measurement, two devices exchange signals and, knowing these signals propagate at the speed of light, the time of arrival is used for distance estimations. Existing distance- measurement techniques are incapable of protecting against adversarial distance enlargement—a highly devastating tactic in which the adversary reissues a delayed version of the signals transmitted between devices, after distorting the authentic signal to prevent the receiver from identifying it. The adversary need not break crypto, nor compromise any upper- layer security protocols for mounting this attack. No known solution currently exists to protect against distance enlargement. We present Ultra-Wideband Enlargement Detection (UWB-ED), a new modulation technique to detect distance enlargement attacks, and securely verify distances between two mutually trusted devices. We analyze UWB-ED under an adversary that injects signals to block/modify authentic signals. We show how UWB-ED is a good candidate for 802.15.4z Low Rate Pulse and the 5G standard.

Physical-layer attacks allow attackers to manipulate (spoof) ranging and positioning. These attacks had real-world impact and allowed car thefts, executions of unauthorized payments and manipulation of navigation. UWB impulse radio, standardized within 802.15.4a,f, has emerged as a prominent technique for precise ranging that allows high operating distances despite power constraints by transmitting multi-pulse symbols. Security of UWB ranging (in terms of the attacker's ability to manipulate the measured distance) has been discussed in the literature and is, since recently also being addressed as a part of the emerging 802.15.4z standard. However, all research so far, as well as security enhancements proposed within this emerging standard face one main limitation: they achieve security through short symbol lengths and sacrifice performance (i.e., limit the maximum distance of measurement), or use longer symbol lengths, therefore sacrificing security. We present UWB with pulse reordering (UWB-PR), the first modulation scheme that secures distance measurement between two mutually trusted devices against all physical-layer distance shortening attacks without sacrificing performance, therefore simultaneously enabling extended range and security. We analyze the security of UWB-PR under the attacker that fully controls the communication channel and show that UWB-PR resists such strong attackers. We evaluate UWB-PR within a UWB system built on top of the IEEE 802.15.4 device and show that it achieves distances of up to 93m with 10cm precision (LoS). UWB-PR is, therefore, a good candidate for the extended mode of the new 802.15.4z Low Rate Pulse standard. Finally, UWB-PR shows that secure distance measurement can be built on top of modulation schemes with longer symbol lengths - so far, this was considered insecure.

Blockchain and cryptocurrency are a hot topic in today’s digital world. In this paper, we create a game theoretic model in continuous time. We consider a dynamic game model of the bitcoin market, where miners or players use mining systems to mine bitcoin by investing electricity into the mining system. Although this work is motivated by BTC, the work presented can be applicable to other mining systems similar to BTC. We propose three concepts of dynamic game theoretic solutions to the model: Social optimum, Nash equilibrium and myopic Nash equilibrium. Using the model that a player represents a single “miner” or a “mining pool”, we develop novel and interesting results for the cryptocurrency world.

Machine Learning has been widely applied in practice, such as disease diagnosis, target detection. Commonly, a good model relies on massive training data collected from different sources. However, the collected data might expose sensitive information. To solve the problem, researchers have proposed many excellent methods that combine machine learning with privacy protection technologies, such as secure multiparty computation(MPC), homomorphic encryption(HE), and differential privacy. In the meanwhile, some other researchers proposed distributed machine learning which allows the clients to store their data locally but train a model collaboratively. The first kind of method focuses on security, but the performance and accuracy remain to be improved, while the second provides higher accuracy and better performance but weaker security, for instance, the adversary can launch membership attacks from the gradients' updates in plaintext. In this paper, we join secret sharing to distributed machine learning to achieve reliable performance, accuracy and high-level security. Next, we design, implement, and evaluate a practical system to jointly learn an accurate model under semi-honest and servers-only malicious adversary security, respectively. And the experiments show our protocols achieve the best overall performance as well.

In this paper, we present a selfish mining attack on the multi-stage blockchain proposed by Palash Sarkar. We provide detailed analysis of computational wastage of honest miners and biased rewards achieved by the selfish pool. In our analysis, we introduce a spy inside an honest pool which is a trivial task. Our spy is responsible for leaking the information of the stage mining from the honest pool to the selfish pool. In our analysis, we consider all the possible configurations of mining namely sequential, parallel and pipelining. In all of these configurations, we show through our mathematical equations as to how a selfish miner can succeed in wasting the computation power of the honest miner and how he can influence the reward of mining. For completeness, we provide an algorithm for performing a selfish mining attack on all the scenarios on multi-stage blockchain. To thwart selfish mining on multi-stage blockchain we redesign the original verification algorithm by introducing a new parameter called the crypto-stamp. We present a new algorithm that uses crypto-stamp during the verification process of the mined stages or blocks and is able to detect with high probability whether the stages or blocks were kept private or not.

Genetic data is an indispensable part of big data, promoting the advancement of life science and biomedicine. Yet, highly private genetic data also brings concerns about privacy risks in data shar- ing. In our work, we adopt the cryptographic prim- itive Secure Function Evaluation (SFE) to address this problem. A secure SFE scheme allows insti- tutions and hospitals to compute a function while preserving the privacy of their input data, and each participant knows nothing but their own input and the final result. In our work, we present privacy-preserving solutions for Human Leukocyte Antigen (HLA) matching and two popular biostatistics tests: Chi-squared test and odds ratio test. We also show that our protocols are compatible with multiple databases simultaneously and could feasibly han- dle larger-scale data up to genome-wide level. This approach may serve as a new way to jointly analyze distributed and restricted genetic data among insti- tutions and hospitals. Meanwhile, it can potentially be extended to other genetic analysis algorithms, allowing individuals to analyze their own genomes without endangering data privacy.

We propose a new method to compare numbers which are encrypted by Homomorphic Encryption (HE). Previously, comparison and min/max functions were evaluated using Boolean functions where input numbers are encrypted bit-wisely. However, the bit-wise encryption methods require relatively expensive computation of basic arithmetic operations such as addition and multiplication. In this paper, we introduce iterative algorithms that approximately compute the min/max and comparison operations of several numbers which are encrypted word-wisely. From the concrete error analyses, we show that our min/max and comparison algorithms have $\Theta(\alpha)$ and $\Theta(\alpha\log\alpha)$ computational complexity to obtain approximate values within an error rate $2^{-\alpha}$, while the previous minimax polynomial approximation method requires the exponential complexity $\Theta(2^{\alpha/2})$ and $\Theta(\sqrt{\alpha}\cdot 2^{\alpha/2})$, respectively. We also show the (sub-)optimality of our min/max and comparison algorithms in terms of asymptotic computational complexity among polynomial evaluations to obtain approximate min/max and comparison results. Our comparison algorithm is extended to several applications such as computing the top-$k$ elements and counting numbers over the threshold in encrypted state. Our new method enables word-wise HEs to enjoy comparable performance in practice with bit-wise HEs for comparison operations while showing much better performance on polynomial operations. Computing an approximate maximum value of any two $\ell$-bit integers encrypted by HEAAN, up to error $2^{\ell-10}$, takes only $1.14$ milliseconds in amortized running time, which is comparable to the result based on bit-wise HEs.

This paper reports the results of survey done on the architecture and functionalities involved in blockchains. Moreover, it reports the results of comparison between proof-of-work-based blockchains, Bitcoin and Ethereum, against federated consensus-based blockchain, Ripple, and proof-of-validation-based blockchain, Tendermint, along the parameters like peer to peer network setup and maintenance, cryptocurrency involved, details of transaction execution and validation, block creation, block validation and consensus protocol and application development.

Estimating that in 10 years time quantum computers capable of breaking public-key cryptography currently considered safe could exist, this threat is already eminent for information that require secrecy for more than 10 years. Considering the time required to standardize, implement and update existing networks signifies the urgency of adopting quantum-safe cryptography. In this work, we investigate the trade-off between network and CPU overhead and the security levels defined by NIST. To do so, we integrate adapted OpenSSL libraries into OpenVPN, and perform experiments on a large variety of quantum-safe algorithms for respectively TLS versions 1.2 and 1.3 using OpenVPN and HTTPS independently. We describe the difficulties we encounter with the integration and we report the experimental performance results, comparing setting up the quantum-safe connection with setting up the connection without additional post-quantum cryptography.

In recent years, the concept of Internet of Things (IoT) network used to enable everyday objects and electronic devices to communicate with each other has been extensively discussed. There are three main types of communication that can be assumed in an IoT network: unicast, group, and broadcast communication. Apart from that, Hamasaki et al. considered geometric characteristics and proposed a method of geometric group key sharing. Thereafter, a key sharing method suitable for sharing a pairwise key by implementing the method proposed by Hamasaki et al. had been proposed by Nishigami et al. However, testing this method, we found that when a node and its fellow nodes are attacked together, the keys of the rest of the nodes will be leaked. Therefore, in this paper, using the feature introduced in geometric group key sharing, we propose a method that enables a pairwise key to be securely shared. In addition, we extend our method of pairwise key sharing to be applicable for group key sharing to achieve a way to share efficiently pairwise, group, and global keys used in broadcast communication. Finally, we evaluate the efficiency of our proposed method.

An `obfuscation' for the encrypted computing context is quantified exactly here, leading to an argument that security against polynomial-time attacks has been achieved for user data, with or without encryption. Encrypted computing is the emerging science and technology of processors that take encrypted inputs to encrypted outputs via encrypted intermediate values (at nearly conventional speeds). The aim is to make user data in general-purpose computing secure against the operator and operating system as potential adversaries. A stumbling block has always been that memory addresses are data and good encryption means the encrypted value varies randomly,and that makes hitting any target in memory problematic without address decryption, but decryption anywhere on the memory path would open up many easily exploitable vulnerabilities. This paper `solves compilation' for processors without address decryption, covering all of ANSI C while satisfying the required security properties and opening up encrypted computing for the standard software tool-chain and infrastructure. The new understanding produces the argument referred to above.

Encrypted computing is an emerging field in which inputs, outputs and intermediates are maintained in encrypted form in a processor, conferring security on user data against the operator and operating system as adversaries, which run unencrypted in the same machine. Systems that pass encrypted addresses to memory without decryption close a major attack vector and allow off-the-shelf memory to be used. But that makes memory unreliable from the program's perspective, as the many different encryptions of a plaintext address access different memory locations that the program sees as the same with varying contents. A clever `obfuscating' compiler solves the problem, opening up the field.

Blockchain, which is a useful tool for providing data integrity, has emerged as an alternative to centralized servers. Concentrating on the integrity of the blockchain, many applications have been developed. Specifically, a blockchain can be utilized in proving the user's identity using its strong integrity. However, since all data in the blockchain is publicly available, it can cause privacy problems if the user's identity is stored in the blockchain unencrypted. Although the encryption of the private information can diminish privacy problems in the blockchain, it is difficult to transparently utilize encrypted user information in the blockchain. To provide integrity and privacy of user information simultaneously in the blockchain, we propose a SIMS (Self-Sovereign Identity Management System) framework based on a zk-SNARK (zero-knowledge Succinct Non-interactive ARgument of Knowledge). In our proposed SIMS, the user information is employed in a privacy-preserving way due to the zero-knowledge property of the zk-SNARK. We construct a SIMS scheme and prove its security. We describe applications of SIMS and demonstrate its practicality through efficient implementations.

In this paper, we propose that countermeasures against instruction-related timing attack would be vulnerable to single-trace attacks, which are presented at ISPEC 2017 and CHES 2019. The countermeasures use determiner to make operations, which leak timing side-channel information, perform in a constant-time. Since determiner is divided into two groups according to secret credentials, it is possible to recover secret credentials by clustering determiner into two groups.

In the Bitcoin white paper, Nakamoto proposed a very simple Byzantine fault tolerant consensus algorithm that is also known as Nakamoto consensus. Despite its simplicity, some existing analysis of Nakamoto consensus appears to be long and involved. In this technical report, we aim to make such analysis simple and transparent so that we can teach senior undergraduate students and graduate students in our institutions. This report is largely based on a 3-hour tutorial given by one of the authors in June 2019.

Probabilistic data structures use space-efficient representations of data in order to (approximately) respond to queries about the data. Traditionally, these structures are accompanied by probabilistic bounds on query-response errors. These bounds implicitly assume benign attack models, in which the data and the queries are chosen non-adaptively, and independent of the randomness used to construct the representation. Yet probabilistic data structures are increasingly used in settings where these assumptions may be violated. This work provides a provable-security treatment of probabilistic data structures in adversarial environments. We give a syntax that captures a wide variety of in-use structures, and our security notions support derivation of error bounds in the presence of powerful attacks. We use our formalisms to analyze Bloom filters, counting (Bloom) filters and count-min sketch data structures. For the traditional version of these, our security findings are largely negative; however, we show that simple embellishments (e.g., using salts or secret keys) yields structures that provide provable security, and with little overhead.

Cluster analysis is one of the most significant unsupervised machine learning tasks, and it is utilized in various fields associated with privacy issues including bioinformatics, finance and image processing. In this paper, we propose a practical solution for privacy-preserving cluster analysis based on homomorphic encryption~(HE). Our work is the first HE solution for the mean-shift clustering algorithm. To reduce the super-linear complexity of the original mean-shift algorithm, we adopt a novel random sampling method called dust sampling which perfectly fits in HE and achieves the linear complexity. We also substitute non-polynomial kernels by a new polynomial kernel so that it can be efficiently computed in HE. The HE implementation of our modified mean-shift clustering algorithm based on the approximate HE scheme HEAAN shows prominent performance in terms of speed and accuracy. It takes about $30$ minutes with $99\%$ accuracy over several public datasets with hundreds of data, and even for the dataset with $262,144$ data it takes only $82$ minutes applying SIMD operations in HEAAN. Our results outperform the previously best known result (SAC 2018) over $400$ times.

We present a range of novel attacks which exploit information about the volume of answers to range queries in encrypted database. Our attacks rely on a strategy which is simple yet robust and effective. We illustrate the robustness of our strategy in a number of ways. We show how i) to adapt the attack for several variations of a basic usage scenario ii) to defeat countermeasures intended to thwart the premise of our basic attack and iii) to perform partial reconstruction of secret data when unique reconstruction is information theoretically impossible. Furthermore, over the state of the art, our attacks require one order of magnitude fewer queries. We show how to improve the attacks even further, under the assumption that some partial information is known to the adversary. We validate experimentally all of our attacks through extensive experiments on real-world medical data and justify theoretically the effectiveness of our strategy for the basic attack scenario. Our new attacks further underscore the difficulty of striking an appropriate functionality-security trade-off for encrypted databases.

We propose security models for everlasting privacy, a property that protects the content of the votes cast in electronic elections against future and powerful adversaries. Initially everlasting privacy was treated synonymously with information theoretic privacy and did not take advantage of the information available to the adversary and his behavior during or after the election. More recent works provided variations of the concept, limiting the view of the future adversary to publicly available data. We consider an adversary that potentially has insider access to private election data as well. We formally express our adversarial model in game based definitions build on top of a generic voting scheme. This allows us to define a stronger version of everlasting privacy and contrast the two main proposals to achieve it, namely perfectly hiding commitment schemes and anonymous channels.

The Lightning Network (LN) is a payment network running as a second layer on top of Bitcoin and other Blockchains. This paper presents the possibility of performing a balance lockdown in the LN due to misbehaving nodes associated to a given channel. We formalize and introduce a practical attack, minimizing the economic cost of the attack. We present results that validate our claims, and provide experimental results and potential countermeasures to handle the problem.

One of the main goals of securing data transmission is focused on the security of cloud data storage. In this paper, we describe several cryptographic techniques which can be used to address the relevant threats and security goals for analyzing cloud computing security. Private semi-trusted clouds, allow researchers to design private clouds by using cryptographic techniques, to protect the semi-trusted ones. Finally, we elaborate on semi-trusted clouds which are related to real-world deployments of cloud resources, and how optimizing cryptographic protocols, would indeed lead to the usage of this certain cloud and therefore practical ways of securing this type of data.

The fast developing Industrial Internet of Things (IIoT) technologies provide a promising opportunity to build large-scale systems to connect numerous heterogeneous devices into the Internet. Most existing IIoT infrastructures are based on a centralized architecture, which is easier for management but cannot effectively support immutable and verifiable services among multiple parties. Blockchain technology provides many desired features for large-scale IIoT infrastructures, such as decentralization, trustworthiness, trackability, and immutability. This paper presents a blockchain-based IIoT architecture to support immutable and verifiable services. However, when applying blockchain technology to the IIoT infrastructure, the required storage space posts a grant challenge to resource-constrained IIoT infrastructures. To address the storage issue, this paper proposes a hierarchical blockchain storage structure, \textit{ChainSplitter}. Specially, the proposed architecture features a hierarchical storage structure where the majority of the blockchain is stored in the clouds, while the most recent blocks are stored in the overlay network of the individual IIoT networks. The proposed architecture seamlessly binds local IIoT networks, the blockchain overlay network, and the cloud infrastructure together through two connectors, the \textit{blockchain connector} and the \textit{cloud connector}, to construct the hierarchical blockchain storage. The blockchain connector in the overlay network builds blocks in blockchain from data generated in IIoT networks, and the cloud connector resolves the blockchain synchronization issues between the overlay network and the clouds. We also provide a case study to show the efficiency of the proposed hierarchical blockchain storage in a practical Industrial IoT case.

Due to their impressive advantages, Radio Frequency IDentification (RFID) systems are ubiquitously found in various novel applications. These applications are usually in need of quick and accurate authentication or identification. In many cases, it has been shown that if such systems are not properly designed, an adversary can cause security and privacy concerns for end-users. In order to deal with these concerns, impressive endeavors have been made which have resulted in various RFID authentications being proposed. In this study, we analyze three lightweight RFID authentication protocols proposed in Wireless Personal Communications (2014), Computers & Security (2015) and Wireless Networks (2016). We show that none of the studied protocols provides the desired security and privacy required by the end-users. We present various security and privacy attacks such as secret parameter reveal, impersonation, DoS, traceability, and forward traceability against the studied protocols. Our attacks are mounted in the Ouafi–Phan RFID formal privacy model which is a modified version of the well-known Juels–Weis privacy model.

Sybil attacks are a well-studied problem in peer-to-peer networking systems. However, their relevance to cryptocurrency mixers has received little attention in the literature, with only a few papers in recent times aiming to design mixers that are resistant to Sybil attacks. A lot of the research has been primarily driven by independent cryptocurrency enthusiasts. We attempt to provide a few characterizations of Sybil attacks as they pertain to mixers and provide mitigations based on economics in order to disincentive Sybil attacks against mixers. In doing so, we highlight that the security of mixers need not only be analyzed through the use of cryptographic techniques but also with the use of economic techniques. Moreover, we provide future research directions in determining heuristics for detecting Sybil identities in mixers.

Most modern web browsers today sacrifice optimal TLS security for backward compatibility. They apply coarse-grained TLS configurations that support (by default) legacy versions of the protocol that have known design weaknesses, and weak ciphersuites that provide fewer security guarantees (e.g. non Forward Secrecy), and silently fall back to them if the server selects to. This introduces various risks including downgrade attacks such as the POODLE attack that exploits the browsers silent fallback mechanism to downgrade the protocol version in order to exploit the legacy version flaws. To achieve a better balance between security and backward compatibility, we propose a mechanism for fine-grained TLS configurations in web browsers based on the sensitivity of the domain name in the HTTPS request using a white listing technique. That is, the browser enforces optimal TLS configurations for connections going to sensitive domains while enforcing default configurations for the rest of the connections. We demonstrate the feasibility of our proposal by implementing a proof-of-concept as a Firefox browser extension. We envision this mechanism as a built-in security feature in web browsers, e.g. a button similar to the “Bookmark” button in Firefox browsers and as a standardised HTTP header, to augment browsers security.

Bitcoin and similar blockchain systems have a limited transaction throughput because each transaction must be processed by all parties, on-chain. Payment channels relieve the blockchain by allowing parties to execute transactions off-chain while maintaining the on-chain security guarantees, i.e., no party can be cheated out of their funds. However, to maintain these guarantees all parties must follow blockchain updates ardently. To alleviate this issue, a channel party can hire a "watchtower" to periodically check the blockchain for fraud on its behalf. However, watchtowers will only do their job properly if there are financial incentives, fees, and punishments. There are known solutions, but these need complex smart contracts, and as such are not applicable to Bitcoin's simple script language. This raises the natural question of whether incentivized watchtowers are at all possible in a system like Bitcoin. In this work, we answer this question affirmatively, by introducing Cerberus channels, an extension of Lightning channels. Cerberus channels reward watchtowers while remaining secure against bribing and collusion; thus participants can safely go offline for an extended period of time. We show that Cerberus channels are correct, and provide a proof-of-concept implementation in the Bitcoin script language.

We demonstrate that XOR Arbiter PUFs with an even number of arbiter chains have inherently biased responses, even if all arbiter chains are perfectly unbiased. This rebukes the believe that XOR Arbiter PUFs are, like Arbiter PUFs, unbiased when ideally implemented and proves that independently manufactured Arbiter PUFs are not statistically independent. As an immediate result of this work, we suggest to use XOR Arbiter PUFs with odd numbers of arbiter chains whenever possible. Furthermore, our analysis technique can be applied to future types of PUF designs and can hence be used to identify design weaknesses, in particular when using Arbiter PUFs as building blocks and when developing designs with challenge pre-processing. Finally, we discuss consequences for the parameter recommendations of the Interpose PUF. Investigating the reason of the systematic bias of XOR Arbiter PUF, we exhibit that Arbiter PUFs suffer from a systematic uniqueness weakness.

We propose OCEAN, an alternative miner reward system for blockchains that seeks to discourage pooling by providing a pool's variance mitigation functionality as a blockchain built-in. Our proposal relies on Succinct, Non-interactive Arguments of Knowledge (SNARKs), an advanced modern cryptographic tool that enables anyone to prove complex statements with the proof not growing in size with the complexity of the statement. We expect that blockchains that implement our proposal would see less pooling centralization than what is currently observable in deployed cryptocurrencies.

Robustness to modeling attacks is an important requirement for PUF circuits. Several reported Arbiter PUF com- positions have resisted modeling attacks. and often require huge computational resources for successful modeling. In this paper we present deep feedforward neural network based modeling attack on 64-bit and 128-bit Arbiter PUF (APUF), and several other PUFs composed of Arbiter PUFs, namely, XOR APUF, Lightweight Secure PUF (LSPUF), Multiplexer PUF (MPUF) and its variants (cMPUF and rMPUF), and the recently proposed Interpose PUF (IPUF, up to the (4,4)-IPUF configuration). The technique requires no auxiliary information (e.g. side-channel information or reliability information), while employing deep neural networks of relatively low structural complexity to achieve very high modeling accuracy at low computational overhead (compared to previously proposed approaches), and is reasonably robust to error-inflicted training dataset.

In recent years, a number of attacks have been developed that can reconstruct encrypted one-dimensional databases that support range queries under the persistent passive adversary model. These attacks allow an (honest but curious) adversary (such as the cloud provider) to find the order of the elements in the database and, in some cases, to even reconstruct the database itself. In this paper we present two mitigation techniques to make it harder for the adversary to reconstruct the database. The first technique makes it impossible for an adversary to reconstruct the values stored in the database with an error smaller than $k/2$, for $k$ chosen by the client. By fine-tuning $k$, the user can increase the adversary's error at will. The second technique is targeted towards adversaries who have managed to learn the distribution of the queries issued. Such adversaries may be able to reconstruct most of the database after seeing a very small (i.e. poly-logarithmic) number of queries. To neutralize such adversaries, our technique turns the database to a circular buffer. All known techniques that exploit knowledge of distribution fail, and no technique can determine which record is first (or last) based on access pattern leakage.

The widespread use of cloud computing has enabled several database providers to store their data on servers in the cloud and answer queries from those servers. In order to protect the confidentiality of data in the cloud, a database can be stored in encrypted form and all queries can be executed on the encrypted database. Recent research results suggest that a curious cloud provider may be able to decrypt some of the items in the database after seeing a large number of queries and their (encrypted) results. In this paper, we focus on one-dimensional databases that support range queries and develop an attack that can achieve full database reconstruction, inferring the exact value of every element in the database. We consider an encrypted database whose records have values from a given universe of $N$ consecutive integers.Our attack assumes access pattern and search pattern leakage. It succeeds after the attacker has seen each of the possible query results at least once, independent of their distribution. If we assume that the client issues queries uniformly at random, we can decrypt the entire database with high probability after observing $O(N^2 \log N)$ queries.

Security and safety-critical remote applications such as e-voting, online banking, industrial control systems and medical devices rely upon user interaction that is typically performed through web applications. Trusted path to such remote systems is critical in the presence of an attacker that controls the computer that the user operates. Such an attacker can observe and modify any IO data without being detected by the user or the server. We investigate the security of previous research proposals and observe several drawbacks that make them vulnerable to attacks. Based on these observations we identify novel requirements for secure IO operation in the presence of a compromised host. As a solution, we propose ProtectIOn, a system that ensures IO integrity using a trusted low-TCB device that sits between the attacker-controlled host and the IO devices. ProtectIOn intercepts the display signal and user inputs from the keyboard and mouse, and overlays secure UI on top of the HDMI frames generated by the untrusted host. The guiding design principles of ProtectIOn are that (i) integrity of user input and output cannot be considered separately, (ii) all user input modalities need to be protected simultaneously, and (iii) integrity protection should not rely on error prone user tasks like checking the presence of security indicators. By following these guidelines, ProtectIOn achieves strong protection for IO integrity. We also propose an extension of ProtectIOn for IO confidentiality and implement a plug-and-play prototype and evaluate its performance.

Reed-Muller codes are among the most important classes of locally correctable codes. Currently local decoding of Reed-Muller codes is based on decoding on lines or quadratic curves to recover one single coordinate. To recover multiple Reed-Muller codes are among the most important classes of locally correctable codes. Currently local decoding of Reed-Muller codes is based on decoding on lines or quadratic curves to recover one single coordinate. To recover multiple coordinates simultaneously, the naive way is to repeat the local decoding for recovery of a single coordinate. This decoding algorithm might be more expensive, i.e., require higher query complexity. %Precisely speaking, to correct arbitrarily large number $k$ of coordinates simultaneously by repetition of local decoding for recovery of a single coordinate, one has to query $\Omega(k\sqrt{q}\log k/\log q)$ coordinates, where $q$ is the code alphabet size. In this paper, we focus on Reed-Muller codes with usual parameter regime, namely, the total degree of evaluation polynomials is $d=\Theta({q})$, where $q$ is the code alphabet size (in fact, $d$ can be as big as $q/4$ in our setting). %the usual parameter regime where the number $m$ of variables of evaluation polynomials is much bigger than the code alphabet size $q$ and total degree of polynomials is $d=\Theta({q})$. By introducing a novel variation of codex, i.e., interleaved codex (the concept of codex has been used for arithmetic secret sharing \cite{C11,CCX12}), we are able to locally recover arbitrarily large number $k$ of coordinates of a Reed-Muller code simultaneously with error probability $\exp(-\Omega(k))$ at the cost of querying merely $O(q^2k)$ coordinates. It turns out that our local decoding of Reed-Muller codes shows ({\it perhaps surprisingly}) that accessing $k$ locations is in fact cheaper than repeating the procedure for accessing a single location for $k$ times. Precisely speaking, %by repetition of local decoding for recovery of a single coordinate, one has to query $\Omega(qk\log k/\log q)$ coordinates (thus, the query complexity of our local decoding becomes cheaper for $k=\Omega(q^q)$). Furthermore, our decoding success probability is $1-\Ge$ with $\Ge=O\left(\left(\frac1{\sqrt{q}}\right)^k\right)$. to get the same success probability by repeating the local decoding algorithm of a single coordinate, one has to query $\Omega(qk^2)$ coordinates. Thus, the query complexity of our local decoding is smaller for $k=\Omega(q)$. If we impose the same query complexity constraint on both algorithm, our local decoding algorithm yields smaller error probability when $k=\Omega(q^q)$. In addition, our local decoding is efficient, i.e., the decoding complexity is $\poly(k,q)$. Construction of an interleaved codex is based on concatenation of a codex with a multiplication friendly pair, while the main tool to realize codex is based on algebraic function fields (or more precisely, algebraic geometry codes).

Modern cryptography requires the ability to securely generate pseudorandom numbers. However, despite decades of work on side channel attacks, there is little discussion of their application to pseudorandom number generators (PRGs). In this work we set out to address this gap, empirically evaluating the side channel resistance of common PRG implementations. We find that hard-learned lessons about side channel leakage from encryption primitives have not been applied to PRGs, at all levels of abstraction. At the design level, the NIST-recommended CTR_DRBG design does not have forward security if an attacker is able to compromise the state via a side-channel attack. At the primitive level, popular implementations of CTR_DRBG such as OpenSSL's FIPS module and NetBSD's kernel use leaky T-table AES as their underlying block cipher, enabling cache side-channel attacks. Finally, we find that many implementations make parameter choices that enable an attacker to fully exploit the side-channel attack in a realistic scenario and recover secret keys from TLS connections. We empirically demonstrate our attack in two scenarios. In the first, we carry out an asynchronous cache attack that recovers the private state from vulnerable CTR_DRBG implementations under realistic conditions to recover long-term authentication keys when the attacker is a party in the TLS connection. In the second scenario, we show that an attacker can exploit the high temporal resolution provided by Intel SGX to carry out a blind attack to recover CTR\_DRBG's state within three AES encryptions, without viewing output, and thus to decrypt passively collected TLS connections from the victim.

In the context of second layer payments in Bitcoin, and specifically the Lightning Network, we propose a design for a lightweight watchtower that does not need to store signed justice transactions. We alter the structure of the opening and commitment transactions in Lightning channels to encode justice transactions as part of the commitment transactions. With that, a watchtower just needs to watch for specific cheating commitment transaction IDs on the blockchain and can extract signed justice transactions directly from these commitment transactions that appear on the blockchain. Our construction saves an order of magnitude in storage over existing watchtower designs. In addition, we let the watchtower prove to each channel that it has access to all the data required to do its job, and can therefore be paid-per-update.

Securing Neural Network (NN) computations through the use of Fully Homomorphic Encryption (FHE) is the subject of a growing interest in both communities. Among different possible approaches to that topic, our work focuses on applying FHE to hide the model of a neural network-based system in the case of a plain input. In this paper, using the TFHE homomorphic encryption scheme, we propose an efficient fully homomorphic method for an argmin computation on an arbitrary number of encrypted inputs and an asymptotically faster - though levelled - equivalent scheme. Using these schemes and a unifying framework for LWE-based homomorphic encryption schemes (Chimera), we implement a very time-wise efficient, homomorphic speaker recognition scheme using the neural-based embedding system VGGVox. This work can be generalized to all other similar Euclidean embedding-based recognition systems. While maintaining the best-of-class classification rate of the VGGVox system, we implement a speaker-recognition system that can classify a speech sample as coming from one of a 100 hidden model speakers in less than one second.

Messaging systems are used to spread misinformation and other malicious content, often with dire consequences. End-to-end encryption improves privacy but hinders content-based moderation and, in particular, obfuscates the original source of malicious content. We introduce the idea of message traceback, a new cryptographic approach that enables platforms to simultaneously provide end-to-end encryption while also being able to track down the source of malicious content reported by users. We formalize functionality and security goals for message traceback, and detail two constructions that allow revealing a chain of forwarded messages (path traceback) or the entire forwarding tree (tree traceback). We implement and evaluate prototypes of our traceback schemes to highlight their practicality, and provide a discussion of deployment considerations.

In this paper we present our observations about NIST's Compression estimate test given in SP-800 90B. We observe that steps 4 and 7 of the test may be re-framed to gain efficiency. Based on our observations, we propose a modified algorithm for the test which is twice as fast as the NIST's algorithm. We further claim that the values of probability and min-entropy in the example given for the test are incorrect. We also provide computational evidence in support of this claim.

Financial deposits are fundamental to the security of cryptoeconomic protocols as they serve as insurance against potential misbehaviour of agents. However, protocol designers and their agents face a trade-off when choosing the deposit size. While substantial deposits might increase the protocol security, for example by minimising the impact of adversarial behaviour or risks of currency fluctuations, locked-up capital incurs opportunity costs. Moreover, some protocols require over-collateralization in anticipation of future events and malicious intentions of agents. We present Balance, an application-agnostic system that reduces over-collateralization without compromising protocol security. In Balance, malicious agents receive no additional utility for cheating once their deposits are reduced. At the same time, honest and rational agents increase their utilities for behaving honestly as their opportunity costs for the locked-up deposits are reduced. Balance is a round-based mechanism in which agents need to continuously perform desired actions. Rather than treating agents' incentives and behaviour as ancillary, we explicitly model agents' utility, proving the conditions for incentive compatibility. Balance improves social welfare given a distribution of honest, rational, and malicious agents. Further, we integrate Balance with a cross-chain interoperability protocol, XCLAIM, reducing deposits by 10% while maintaining the same utility for behaving honestly. Our implementation allows any number of agents to be maintained for at most 55,287 gas (ca. USD 0.07) to update all agents' scores, and at a cost of 54,948 gas (ca. USD 0.07) to update the assignment of all agents to layers.

We present a new primitive supporting file replication in distributed storage networks (DSNs) called a Public Incompressible Encoding (PIE). PIEs operate in the challenging public DSN setting where files must be encoded and decoded with public randomness—i.e., without encryption—and retention of redundant data must be publicly verifiable. They prevent undetectable data compression, allowing DSNs to use monetary rewards or penalties in incentivizing economically rational servers to properly replicate data. Their definition also precludes critical, demonstrated attacks involving parallelism via ASICs and other custom hardware. Our PIE construction is the first to achieve experimentally validated near-optimal performance—within a factor of 4 of optimal by one metric. It also allows decoding orders of magnitude faster than encoding, unlike other comparable constructions. We achieve this high security and performance using a graph construction called a Dagwood Sandwich Graph (DSaG), built from a novel interleaving of depth-robust graphs and superconcentrators. PIEs' performance makes them appealing for DSNs, such as the proposed Filecoin system and Ethereum data sharding. Conversely, their near-optimality establishes concerning bounds on the practical financial and energy costs of DSNs allowing arbitrary data.