Markets for Database Privacy

Database privacy has garnered a recent surge in interest from the theoretical computer science community following the seminal work of [DMNS06], which proposed the strong notion of differential privacy. Classical differentially private mechanisms produce a noisy statistic on an input database that is ε-differentially private for some input parameter ε. This work continues a new line of research seeking to develop mechanisms that endogenously produce a value of ε that depends on the privacy preferences of the data owners and analyst. For such mechanisms, I propose a new notion of endogenous differential privacy, which requires a mechanism to satisfy ε-differential privacy on neighboring databases only for the ε in the mechanism’s privacy support of one of those databases. I develop a framework for markets that 1) determine ε using the privacy and accuracy preferences of a heterogeneous body of data owners and a single analyst, 2) compute a noisy statistic on the database, and 3) collect and distribute payments for the chosen level of ε. I present and analyze one simple class of markets for database privacy in this framework. These mechanisms directly elicit data owners’ true preferences, they determine a level of ε that is efficient given data owner and analyst preferences, and they are endogenously differentially private with respect to data owners’ privacy preferences as well as their private data.