Privacy-Preserving Transactions with Verifiable Local Differential Privacy

Abstract

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.