Differentially Private Policy Evaluation
  We present the first differentially private algorithms for reinforcement learning, which apply to the task of evaluating a fixed policy. We establish two approaches for achieving differential privacy, provide a theoretical analysis of the privacy and utility of the two algorithms, and show promising results on simple empirical examples. 
Borja Balle, Maziar Gomrokchi, Doina Precup
cs.LG stat.ML