Wasserstein Markets for Differentially-Private Data

TL;DR

This paper introduces a novel valuation mechanism for differentially-private data using Wasserstein distance, enabling efficient data markets that balance privacy and utility without requiring trusted third parties.

Contribution

It proposes a Wasserstein distance-based valuation mechanism and incentive-compatible procurement methods for privacy-preserving data markets, addressing limitations of existing frameworks.

Findings

Mechanisms are reformulated as tractable mixed-integer second-order cone programs.

Validated through numerical studies demonstrating effectiveness.

Addresses combinatorial data value and differential privacy effects.

Abstract

Data is an increasingly vital component of decision making processes across industries. However, data access raises privacy concerns motivating the need for privacy-preserving techniques such as differential privacy. Data markets provide a means to enable wider access as well as determine the appropriate privacy-utility trade-off. Existing data market frameworks either require a trusted third party to perform computationally expensive valuations or are unable to capture the combinatorial nature of data value and do not endogenously model the effect of differential privacy. This paper addresses these shortcomings by proposing a valuation mechanism based on the Wasserstein distance for differentially-private data, and corresponding procurement mechanisms by leveraging incentive mechanism design theory, for task-agnostic data procurement, and task-specific procurement co-optimisation. The mechanisms are reformulated into tractable mixed-integer second-order cone programs, which are validated with numerical studies.