Wang, Zhipeng;
Chaliasos, Stefanos;
Qin, Kaihua;
Zhou, Liyi;
Gao, Lifeng;
Berrang, Pascal;
Livshits, Benjamin;
(2023)
On How Zero-Knowledge Proof Blockchain Mixers Improve, and Worsen User Privacy.
In: Ding, Ying and Tang, Jie and Sequeda, Juan and Aroyo, Lora and Castillo, Carlos and Houben, Geert-Jan, (eds.)
WWW '23: Proceedings of the ACM Web Conference 2023.
(pp. pp. 2022-2032).
ACM (Association for Computing Machinery): New York, NY, USA.
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Abstract
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 . 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 and 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.
| Type: | Proceedings paper |
|---|---|
| Title: | On How Zero-Knowledge Proof Blockchain Mixers Improve, and Worsen User Privacy |
| Event: | WWW '23: The ACM Web Conference 2023 |
| ISBN-13: | 9781450394161 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3543507.3583217 |
| Publisher version: | http://dx.doi.org/10.1145/3543507.3583217 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Blockchain; Privacy; Anonymity; Mixer; DeFi |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10184999 |
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