Differentially Private Call Auctions and Market Impact
Computer Science and Game Theory
2020-02-14 v1 Computational Engineering, Finance, and Science
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.
Cite
@article{arxiv.2002.05699,
title = {Differentially Private Call Auctions and Market Impact},
author = {Emily Diana and Hadi Elzayn and Michael Kearns and Aaron Roth and Saeed Sharifi-Malvajerdi and Juba Ziani},
journal= {arXiv preprint arXiv:2002.05699},
year = {2020}
}