Multiple Privacy Regimes Mechanism for Local Differential Privacy

Abstract

Local differential privacy (LDP), as a state-of-the-art privacy notion, enables users to share protected data safely while the private real data never leaves user’s device. The privacy regime is one of the critical parameters balancing between the correctness of the statistical result and the level of user’s privacy. In the majority of current work, authors assume that the privacy regime is totally determined by the service provider and dispatched to all users. However, it is inelegant and unpromising for all users to accept the same privacy level in real world. In this paper, we propose a new LDP estimation method MLE which is applicable for the scenario of multiple privacy regimes. MLE uses the idea of parameter estimation to merge the results generated by users of different privacy levels. We also propose an extension of MLE to handle the situation when all users’ regimes are in a continuous distribution. We also provide an Adapt estimator which assigns users to use different LDP schemes based on their regimes, and it performs better than the estimator with only one fixed LDP scheme. Experiments show that our methods provide a higher level of accuracy than previous proposals in this multiple regimes scenario.