Private Mechanism Design via Quantile Estimation

Yuanyuan Yang, Tao Xiao, Bhuvesh Kumar, Jamie Heather Morgenstern

Keywords: online auctions, differential privacy, mechanism design

TL;DR: we demonstrate how to efficiently learn a near-optimal mechanism with pure privacy from sample data for the single item setting. We further apply this mechanism to online settings involving non-myopic and strategic bidders.

Abstract:

We investigate the problem of designing differentially private (DP), revenue-maximizing single item auction. Specifically, we consider broadly applicable settings in mechanism design where agents' valuation distributions are independent, non-identical, and can be either bounded or unbounded. Our goal is to design such auctions with pure, i.e., $(\epsilon,0)$ privacy in polynomial time.

In this paper, we propose two computationally efficient auction learning framework that achieves pure privacy under bounded and unbounded distribution settings. These frameworks reduces the problem of privately releasing a revenue-maximizing auction to the private estimation of pre-specified quantiles. Our solutions increase the running time by polylog factors compared to the non-private version. As an application, we show how to extend our results to the multi-round online auction setting with non-myopic bidders. To our best knowledge, this paper is the first to efficiently deliver a Myerson auction with pure privacy and near-optimal revenue, and the first to provide such auctions for unbounded distributions.

Primary Area: optimization

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Submission Number: 2239