Abstract:
We propose and analyze differentially private (DP) mechanisms for call auctions as an alternative to the complex and ad-hoc privacy efforts that are common in modern electronic markets. We prove that the number of shares cleared in the DP mechanisms compares favorably to the non-private optimal and provide a matching lower bound. We analyze the incentive properties of our mechanisms and their behavior under natural no-regret learning dynamics by market participants. We include simulation results and connections to the finance literature on market impact.
Bio:
Michael Kearns is a Professor and the National Center Chair in the Computer and Information Science Department at the University of Pennsylvania, where his research interests include machine learning, algorithmic game theory, quantitative finance and related topics. He has secondary appointments in Penn’s Economics Department, and in the departments of Statistics and Operations, and Information and Decisions (OID) at the Wharton School. Kearns also has a role at Amazon as part of its Amazon Scholars program, focusing on algorithmic fairness, privacy, machine learning, and related topics within Amazon Web Services. He is the co-author of the general-audience book "The Ethical Algorithm" (with Aaron Roth; Oxford University Press 2019).
Kearns has worked extensively in quantitative and algorithmic trading on Wall Street (including at Lehman Brothers, Bank of America, SAC Capital, and Morgan Stanley). In the past he has served as an adviser to technology companies and venture capital firms. He has also occasionally served as an expert witness or consultant on technology-related legal and regulatory cases. Kearns is an elected fellow of the National Academy of Sciences, the American Academy of Arts and Sciences, the Association for Computing Machinery, the Association for the Advancement of Artificial Intelligence, and the Society for the Advancement of Economic Theory.
