论文标题
关于零知识证明区块链搅拌机的改善和恶化的用户隐私
On How Zero-Knowledge Proof Blockchain Mixers Improve, and Worsen User Privacy
论文作者
论文摘要
零知识证明(ZKP)搅拌机是最广泛使用的区块链隐私解决方案之一,在具有智能合同的区块链之上运行。我们发现,ZKP混合器与越来越多的分散融资(DEFI)攻击和可提取值(BEV)提取的次数紧密交织在一起。通过硬币流跟踪,我们发现205个区块链攻击者和2,595个BEV提取器将其作为资金来源,同时存入4.1287亿美元的总攻击收入。此外,美国对最大的ZKP搅拌机Tornado.Cash的OFAC制裁已将调音台的日常沉积物减少了80%以上。 此外,ZKP混合器通过所谓的匿名集大小宣传其隐私水平,与K匿名性类似,该设置允许用户隐藏在其他用户集中。但是,通过经验测量,我们发现这些匿名集主张大多是不准确的。对于以太坊(ETH)和Binance Smart Chain(BSC)上最受欢迎的搅拌机,我们展示了如何平均将匿名设置的大小降低27.34%和46.02%。我们的经验证据也是第一个在ETH和BSC上提出不同隐私性推荐的人。 此外,最先进的ZKP搅拌机通过提供匿名采矿(AM)激励措施,即用户获得混合硬币的货币奖励,与Defi生态系统交织在一起。但是,与相关工作的主张相反,我们发现AM不一定会提高混音器匿名集的质量。我们的发现表明,吸引了隐私的用户,他们不为改善其他混音器用户的隐私贡献。
Zero-knowledge proof (ZKP) mixers are one of the most widely-used blockchain privacy solutions, operating on top of smart contract-enabled blockchains. We find that ZKP mixers are tightly intertwined with the growing number of Decentralized Finance (DeFi) attacks and Blockchain Extractable Value (BEV) extractions. Through coin flow tracing, we discover that 205 blockchain attackers and 2,595 BEV extractors leverage mixers as their source of funds, while depositing a total attack revenue of 412.87M USD. Moreover, the US OFAC sanctions against the largest ZKP mixer, Tornado.Cash, have reduced the mixer's daily deposits by more than 80%. Further, ZKP mixers advertise their level of privacy through a so-called anonymity set size, which similarly to k-anonymity allows a user to hide among a set of k other users. Through empirical measurements, we, however, find that these anonymity set claims are mostly inaccurate. For the most popular mixers on Ethereum (ETH) and Binance Smart Chain (BSC), we show how to reduce the anonymity set size on average by 27.34% and 46.02% respectively. Our empirical evidence is also the first to suggest a differing privacy-predilection of users on ETH and BSC. State-of-the-art ZKP mixers are moreover interwoven with the DeFi ecosystem by offering anonymity mining (AM) incentives, i.e., users receive monetary rewards for mixing coins. However, contrary to the claims of related work, we find that AM does not necessarily improve the quality of a mixer's anonymity set. Our findings indicate that AM attracts privacy-ignorant users, who then do not contribute to improving the privacy of other mixer users.