Selling Data at an Auction under Privacy Constraints
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.
Keywords
Cite
@article{arxiv.2005.09248,
title = {Selling Data at an Auction under Privacy Constraints},
author = {Mengxiao Zhang and Fernando Beltran and Jiamou Liu},
journal= {arXiv preprint arXiv:2005.09248},
year = {2020}
}