TOPCOAT: towards practical two-party Crystals-Dilithium

IF 1.7 3区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Nikita Snetkov, Jelizaveta Vakarjuk, Peeter Laud
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引用次数: 0

Abstract

The development of threshold protocols based on lattice-signature schemes has been of increasing interest in the past several years. The main research focus has been towards protocols constructed for various variants of Crystals-Dilithium, future NIST digital signature standard known as ML-DSA. In this work, we propose TOPCOAT, a two-party lattice-based signature algorithm that embodies Dilithium’s compression techniques. The aforesaid result is achieved by introducing a new hinting mechanism that allows parties to collaboratively calculate \(\textsf {HighBits}\). Our hinting mechanism allows public key compression similar to Dilithium. Additionally, we suggest an optimization technique to minimize number of restarts both parties need to produce a valid signature. Our approach allows to produce \(\approx 10\) KB signatures within 3 rounds of communication. We prove security of our scheme under MLWE and MSIS assumptions in ROM, and provide implementation of our proposed scheme. As additional contribution, we present vulnerabilities and inconsistencies found in Liu et al. work (Future Generation Computer Systems 2023) which aimed to construct distributed lattice-based signature protocol.

Abstract Image

TOPCOAT:走向实用的两方晶体--锂
在过去几年里,基于晶格签名方案的阈值协议的开发越来越受到关注。主要的研究重点是为晶体-锂(未来的 NIST 数字签名标准,即 ML-DSA)的各种变体构建协议。在这项工作中,我们提出了 TOPCOAT,这是一种基于晶格的两方签名算法,体现了 Dilithium 的压缩技术。上述结果是通过引入一种新的提示机制实现的,这种机制允许双方协同计算 \(\textsf {HighBits}\) 。我们的提示机制可以实现与 Dilithium 类似的公钥压缩。此外,我们还提出了一种优化技术,以尽量减少双方为生成有效签名而重新启动的次数。我们的方法允许在 3 轮通信内生成 \(\approx 10\) KB 签名。我们证明了我们的方案在 ROM 中的 MLWE 和 MSIS 假设下的安全性,并提供了我们提出的方案的实现。作为额外的贡献,我们介绍了在 Liu 等人的工作(未来计算机系统 2023)中发现的漏洞和不一致之处,该工作旨在构建基于网格的分布式签名协议。
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来源期刊
Information Retrieval Journal
Information Retrieval Journal 工程技术-计算机:信息系统
CiteScore
6.20
自引率
0.00%
发文量
17
审稿时长
13.5 months
期刊介绍: The journal provides an international forum for the publication of theory, algorithms, analysis and experiments across the broad area of information retrieval. Topics of interest include search, indexing, analysis, and evaluation for applications such as the web, social and streaming media, recommender systems, and text archives. This includes research on human factors in search, bridging artificial intelligence and information retrieval, and domain-specific search applications.
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