Wasserstein Markets for Differentially-Private Data
This work addresses privacy concerns in data markets by enabling task-agnostic and task-specific procurement without trusted third parties, though it is incremental in improving existing frameworks.
The paper tackles the problem of valuing and procuring differentially-private data in markets by proposing a valuation mechanism based on Wasserstein distance and corresponding procurement mechanisms, validated through numerical studies with tractable reformulations.
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.