Selling Privacy at Auction
Arpita Ghosh, Aaron Roth

TL;DR
This paper develops a theoretical framework for markets of private data using differential privacy, designing truthful auctions that optimize data acquisition costs and accuracy under budget constraints.
Contribution
It introduces a novel auction-based model for private data markets, linking privacy valuation with procurement auctions, and provides mechanisms that are optimal or near-optimal for specific goals.
Findings
Auctions can be modeled as multi-unit procurement problems.
Vickrey auction achieves accuracy with minimal payment.
Mechanisms can maximize accuracy within budget constraints.
Abstract
We initiate the study of markets for private data, though the lens of differential privacy. Although the purchase and sale of private data has already begun on a large scale, a theory of privacy as a commodity is missing. In this paper, we propose to build such a theory. Specifically, we consider a setting in which a data analyst wishes to buy information from a population from which he can estimate some statistic. The analyst wishes to obtain an accurate estimate cheaply. On the other hand, the owners of the private data experience some cost for their loss of privacy, and must be compensated for this loss. Agents are selfish, and wish to maximize their profit, so our goal is to design truthful mechanisms. Our main result is that such auctions can naturally be viewed and optimally solved as variants of multi-unit procurement auctions. Based on this result, we derive auctions for two…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Mobile Crowdsensing and Crowdsourcing · Auction Theory and Applications
