Differentially Private Policy Evaluation

Learning how to make decisions under uncertainty is becoming paramount in many practical applications, such as medical treatment design, energy management, adaptive user interfaces, recommender systems etc. Reinforcement learning [Sutton and Barto, 1998] provides a variety of algorithms capable of handling such tasks. However, in many practical applications, aside from obtaining good predictive performance, one might also require that the data used to learn the predictor be kept confidential. This is especially true in medical applications, where patient confidentiality is very important, and in other applications which are user-centric (such as recommender systems). Differential privacy (DP) [Dwork, 2006] is a very active research area, originating from cryptography, but which has now been embraced by the machine learning community. DP is a formal model of privacy used to design mechanisms that reduce the amount of information leaked by the result of queries to a database containing sensitive information about multiple users [Dwork, 2006].