Privacy-preserving Analytics for Data Markets using MPC

TL;DR

This paper proposes a cryptographic architecture for privacy-preserving data markets using multi-party computation, addressing GDPR constraints and analyzing privacy risks.

Contribution

It introduces a novel architecture for personal data markets leveraging MPC and provides a privacy-risk analysis following LINDDUN methodology.

Findings

Design of a privacy-preserving data market architecture

Analysis of privacy risks using LINDDUN methodology

Discussion of GDPR compliance considerations

Abstract

Data markets have the potential to foster new data-driven applications and help growing data-driven businesses. When building and deploying such markets in practice, regulations such as the European Union's General Data Protection Regulation (GDPR) impose constraints and restrictions on these markets especially when dealing with personal or privacy-sensitive data. In this paper, we present a candidate architecture for a privacy-preserving personal data market, relying on cryptographic primitives such as multi-party computation (MPC) capable of performing privacy-preserving computations on the data. Besides specifying the architecture of such a data market, we also present a privacy-risk analysis of the market following the LINDDUN methodology.