Georgia Institute of Technology College of Sciences

Database privacy has garnered a recent surge in interest from the theoretical science community following the seminal work of Dwork 2006, which proposed the strong notion of differential privacy. In this setting, each row of a database corresponds to the data owned by some (distinct) individual. An analyst submits a database query to a differentially private mechanism, which replies with a noisy answer guaranteeing privacy for the data owners and accuracy for the analyst. The mechanism's privacy parameter \epsilon is correlated negatively with privacy and positively with accuracy.This work builds a framework for creating and analyzing a market that 1) solves for some socially efficient value of \epsilon using the privacy and accuracy preferences of a heterogeneous body of data owners and a single analyst, 2) computes a noisy statistic on the database, and 3) collects and distributes payments for privacy that elicit truthful reporting of data owners' preferences. We present a market for database privacy in this new framework expanding on the public goods market of Groves and Ledyard, 1977.