针对受监管的保护隐私的加密货币的恶意监管机构的可链接组签名

IF 3 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Xiao Wang , Yanqi Zhao , Lingyue Zhang , Min Xie , Yong Yu , Huilin Li
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引用次数: 0

摘要

随着洗钱、勒索等非法行为的出现,对加密货币隐私保护的监管变得越来越重要。然而,现有的受监管的保护隐私的加密货币通常依赖于单一的监管机构,一旦监管机构腐败,这将严重威胁用户的隐私。为了解决这个问题,我们提出了一种针对恶意监管机构(ALGS)的可链接组签名,用于受监管的保护隐私的加密货币。具体来说,一组监管机构共同监管用户在加密货币交易中的行为。即使有一定数量的监管机构被破坏,我们的方案仍然可以确保合法用户的身份安全。同时,我们的方案可以防止加密货币交易中的双重支出。首先提出了ALGS模型,并定义了其安全特性。在此基础上,提出了一种具有CCA-2匿名性、可追溯性、不可帧性和可链接性的ALGS的具体结构。最后通过与其他方案的比较,对我们的ALGS方案进行了评价,并报告了其优点。实现结果表明,我们的签名算法的运行时间与Emura等人(2017)相比减少了17%,与KSS19 (Krenn等人,2019)相比减少了49%,而验证时间与Emura等人相比减少了31%,与KSS19相比减少了47%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Linkable group signatures against malicious regulators for regulated privacy-preserving cryptocurrencies

Linkable group signatures against malicious regulators for regulated privacy-preserving cryptocurrencies
With the emergence of illegal behaviors such as money laundering and extortion, the regulation of privacy-preserving cryptocurrency has become increasingly important. However, existing regulated privacy-preserving cryptocurrencies usually rely on a single regulator, which seriously threatens users’ privacy once the regulator is corrupt. To address this issue, we propose a linkable group signature against malicious regulators (ALGS) for regulated privacy-preserving cryptocurrencies. Specifically, a set of regulators work together to regulate users’ behavior during cryptocurrencies transactions. Even if a certain number of regulators are corrupted, our scheme still ensures the identity security of a legal user. Meanwhile, our scheme can prevent double-spending during cryptocurrency transactions. We first propose the model of ALGS and define its security properties. Then, we present a concrete construction of ALGS, which provides CCA-2 anonymity, traceability, non-frameability, and linkability. We finally evaluate our ALGS scheme and report its advantages by comparing other schemes. The implementation result shows that the runtime of our signature algorithm is reduced by 17% compared to Emura et al. (2017) and 49% compared to KSS19 (Krenn et al. 2019), while the verification time is reduced by 31% compared to Emura et al. and 47% compared to KSS19.
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CiteScore
4.70
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