Selling Data at an Auction under Privacy Constraints

TL;DR

This paper introduces the SingleMindedQuery (SMQ) mechanism, a reverse auction approach for data marketplaces that respects owners' privacy requirements while maximizing data utility and ensuring incentive compatibility.

Contribution

It proposes a novel auction mechanism for privacy-aware data purchasing that guarantees incentive compatibility, individual rationality, and budget feasibility.

Findings

SMQ effectively balances privacy constraints and data utility.

Experimental results validate the mechanism's performance.

SMQ ensures truthful reporting and fair compensation.

Abstract

Private data query combines mechanism design with privacy protection to produce aggregated statistics from privately-owned data records. The problem arises in a data marketplace where data owners have personalised privacy requirements and private data valuations. We focus on the case when the data owners are single-minded, i.e., they are willing to release their data only if the data broker guarantees to meet their announced privacy requirements. For a data broker who wants to purchase data from such data owners, we propose the SingleMindedQuery (SMQ) mechanism, which uses a reverse auction to select data owners and determine compensations. SMQ satisfies interim incentive compatibility, individual rationality, and budget feasibility. Moreover, it uses purchased privacy expectation maximisation as a principle to produce accurate outputs for commonly-used queries such as counting, median and linear predictor. The effectiveness of our method is empirically validated by a series of experiments.