Paper 2024/326 Original: eprint.iacr.org/2024/326 ↗
Haven++: Batched and Packed Dual-Threshold Asynchronous Complete Secret Sharing with Applications
Abstract
Asynchronous complete secret sharing (ACSS) is a foundational primitive in the design of distributed algorithms and cryptosystems that require confidentiality. ACSS permits a dealer to distribute a secret to a collection of $n$ servers so that everyone holds shares of a polynomial containing the dealer's secret. This work contributes a new ACSS protocol, called Haven++, that uses packing and batching to make asymptotic and concrete advances in the design and application of ACSS for large secrets. Haven++ allows the dealer to pack multiple secrets in a single sharing phase, and to reconstruct either one or all of them later. For even larger secrets, we contribute a batching technique to amortize the cost of proof generation and verification across multiple invocations of our protocol. The result is an asymptotic improvement in the worst-case amortized communication and computation complexity, both for ACSS itself and for its application to asynchronous distributed key generation. Our ADKG based on Haven++ achieves, for the first time, an optimal worst case amortized communication complexity of $O(\kappa n)$ without a trusted setup. To show the practicality of Haven++, we implement it and find that it outperforms the work of Yurek et al.\ (NDSS 2022) by more than an order of magnitude when there are malicious, faulty parties.
Metadata
- Available format(s)
PDF
- Category
- Cryptographic protocols
- Publication info
- Preprint.
- History
- 2025-02-25: last updated
- 2024-02-26: published
- Short URL
- https://ia.cr/2024/326
- License
- https://creativecommons.org/licenses/by/4.0/
BibTeX
@misc{cryptoeprint:2024/326,
author = {Nicolas Alhaddad and Mayank Varia and Ziling Yang},
title = {Haven++: Batched and Packed Dual-Threshold Asynchronous Complete Secret Sharing with Applications},
howpublished = {Cryptology {ePrint} Archive, Paper 2024/326},
year = {2024},
url = {https://eprint.iacr.org/2024/326}
}