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We give new mechanisms for answering exponentially many queries from multipleanalysts on a private database, while protecting differential privacy both forthe individuals in the database and for the analysts. That is, our mechanism'sanswer to each query is nearly insensitive to changes in the queries asked byother analysts. Our mechanism is the first to offer differential privacy on thejoint distribution over analysts' answers, providing privacy for data analystseven if the other data analysts collude or register multiple accounts. In somesettings, we are able to achieve nearly optimal error rates (even compared tomechanisms which do not offer analyst privacy), and we are able to extend ourtechniques to handle non-linear queries. Our analysis is based on a novel viewof the private query-release problem as a two-player zero-sum game, which maybe of independent interest.

Differential Privacy for the Analyst via Private Equilibrium Computation