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Pay Less for Your Privacy: Towards Cost-Effective On-Chain Mixers

Authors Zhipeng Wang, Marko Cirkovic, Duc V. Le, William Knottenbelt, Christian Cachin

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Author Details

Zhipeng Wang
  • Imperial College London, UK
Marko Cirkovic
  • University of Bern, Switzerland
Duc V. Le
  • Visa Research, Sunnyvale, CA, USA
William Knottenbelt
  • Imperial College London, UK
Christian Cachin
  • University of Bern, Switzerland

Funding

  • Le, Duc V.: Supported by a grant from Protocol Labs to the University of Bern.

Acknowledgements

The authors thank anonymous reviewers for helpful feedback.

Cite As Get BibTex

Zhipeng Wang, Marko Cirkovic, Duc V. Le, William Knottenbelt, and Christian Cachin. Pay Less for Your Privacy: Towards Cost-Effective On-Chain Mixers. In 5th Conference on Advances in Financial Technologies (AFT 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 282, pp. 16:1-16:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.AFT.2023.16

Abstract

On-chain mixers, such as Tornado Cash (TC), have become a popular privacy solution for many non-privacy-preserving blockchain users. These mixers enable users to deposit a fixed amount of coins and withdraw them to another address, while effectively reducing the linkability between these addresses and securely obscuring their transaction history. However, the high cost of interacting with existing on-chain mixer smart contracts prohibits standard users from using the mixer, mainly due to the use of computationally expensive cryptographic primitives. For instance, the deposit cost of TC on Ethereum is approximately 1.1M gas (i.e., 66 USD in June 2023), which is 53× higher than issuing a base transfer transaction.
In this work, we introduce the Merkle Pyramid Builder approach, to incrementally build the Merkle tree in an on-chain mixer and update the tree per batch of deposits, which can therefore decrease the overall cost of using the mixer. Our evaluation results highlight the effectiveness of this approach, showcasing a significant reduction of up to 7× in the amortized cost of depositing compared to state-of-the-art on-chain mixers. Importantly, these improvements are achieved without compromising users' privacy. Furthermore, we propose the utilization of verifiable computations to shift the responsibility of Merkle tree updates from on-chain smart contracts to off-chain clients, which can further reduce deposit costs. Additionally, our analysis demonstrates that our designs ensure fairness by distributing Merkle tree update costs among clients over time.

Subject Classification

ACM Subject Classification
  • Security and privacy → Pseudonymity, anonymity and untraceability
Keywords
  • Privacy
  • Blockchain
  • Mixers
  • Merkle Tree

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