Differentially-Private Collaborative Online Personalized Mean Estimation

We consider the problem of collaborative personalized mean estimation under a privacy constraint in an environment of several agents continuously receiving data according to arbitrary unknown agent-specific distributions. In particular, we provide a method based on hypothesis testing coupled with differential privacy. Two privacy mechanisms are proposed and we provide a theoretical convergence analysis of the proposed algorithm for any bounded unknown distributions on the agents’ data. Numerical results show that for a considered scenario the proposed approach converges much faster than a fully local approach where agents do not share data, and performs comparably to ideal performance where all data is public. This illustrates the benefit of private collaboration in an online setting.