1 research outputs found
Differentially Private Call Auctions and Market Impact
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