Welcome to the resource topic for 2023/126
Title:
Privacy-Preserving Payment System With Verifiable Local Differential Privacy
Authors: Danielle Movsowitz Davidow, Yacov Manevich
Abstract:In permissioned digital currencies such as Central Bank Digital Currencies (CBDCs), data disclosure is essential for gathering aggregated statistics about the transactions and activities of the users. These statistics are later used to set regulations. Differential privacy techniques have been proposed to preserve individuals’ privacy while still making aggregative statistical analysis possible. Recently, privacy-preserving payment systems fit for CBDCs have been proposed. While preserving the privacy of the sender and recipient, they also prevent any insightful learning from their data. Thus, they are ill-qualified to be incorporated with a system that mandates publishing statistical data.
We show that differential privacy and privacy-preserving payments can coexist even if one of the participating parties (i.e., the user or the data analyst) is malicious. We propose a modular scheme that incorporates verifiable local differential privacy techniques into a privacy-preserving payment system. Thus, although the noise is added directly by the user (i.e., the data subject), we prevent her from manipulating her response and enforce the integrity of the noise generation via a novel protocol.
ePrint: https://eprint.iacr.org/2023/126
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