Privacy-Preserving Transactions with Verifiable Local Differential Privacy

Danielle Movsowitz Davidow*, Yacov Manevich*, Eran Toch*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publication5th Conference on Advances in Financial Technologies, AFT 2023
EditorsJoseph Bonneau, S. Matthew Weinberg
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
ISBN (Electronic)9783959773034
StatePublished - 1 Oct 2023
Event5th Conference on Advances in Financial Technologies, AFT 2023 - Princeton, United States
Duration: 23 Oct 202325 Oct 2023

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
ISSN (Print)1868-8969


Conference5th Conference on Advances in Financial Technologies, AFT 2023
Country/TerritoryUnited States


  • Blockchain
  • Differential Privacy
  • Privacy Preserving
  • Verifiable Privacy


Dive into the research topics of 'Privacy-Preserving Transactions with Verifiable Local Differential Privacy'. Together they form a unique fingerprint.

Cite this