Privacy-Preserving Transactions with Verifiable Local Differential Privacy

Authors Danielle Movsowitz Davidow , Yacov Manevich , Eran Toch

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Danielle Movsowitz Davidow
  • Tel-Aviv University, Israel
Yacov Manevich
  • IBM Research - Zürich, Switzerland
Eran Toch
  • Tel-Aviv University, Israel


We would also like to thank Dany Moshkovich and the reviewers of this paper for their helpful comments and thorough review.

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Danielle Movsowitz Davidow, Yacov Manevich, and Eran Toch. Privacy-Preserving Transactions with Verifiable Local Differential Privacy. In 5th Conference on Advances in Financial Technologies (AFT 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 282, pp. 1:1-1:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


Privacy-preserving transaction systems on blockchain networks like Monero or Zcash provide complete transaction anonymity through cryptographic commitments or encryption. While this secures privacy, it inhibits the collection of statistical data, which current financial markets heavily rely on for economic and sociological research conducted by central banks, statistics bureaus, and research companies. Differential privacy techniques have been proposed to preserve individuals' privacy while still making aggregate analysis possible. We show that differential privacy and privacy-preserving transactions can coexist. We propose a modular scheme incorporating verifiable local differential privacy techniques into a privacy-preserving transaction system. We devise a novel technique that, on the one hand, ensures unbiased randomness and integrity when computing the differential privacy noise by the user and on the other hand, does not degrade the user’s privacy guarantees.

Subject Classification

ACM Subject Classification
  • Security and privacy → Privacy-preserving protocols
  • Security and privacy → Trust frameworks
  • Differential Privacy
  • Blockchain
  • Privacy Preserving
  • Verifiable Privacy


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