Marginal Release Under Multi-party Personalized Differential Privacy

Abstract

Given a set of local datasets held by multiple parties, we study the problem of learning marginals over the integrated dataset while satisfying differential privacy for each local dataset. Different from existing works in the multi-party setting, our work allows the parties to have different privacy preferences for their data, which is referred to as the multi-party personalized differential privacy (PDP) problem. The existing solutions to PDP problems in the centralized setting mostly adopt sampling-based approaches. However, extending similar ideas to the multi-party setting cannot satisfactorily solve our problem. On the one hand, the data owned by multiple parties are usually not identically distributed. Sampling-based approaches will incur a serious distortion in the results. On the other hand, when the parties hold different attributes of the same set of individuals, sampling at the tuple level cannot meet parties’ personalized privacy requirements for different attributes. To address the above problems, we first present a mixture-of-multinomials-based marginal calculation approach, where the global marginals over the stretched datasets are formalized as a multinomial mixture model. As such, the global marginals over the original datasets can be reconstructed based on the calculated model parameters with high accuracy. We then propose a privacy budget segmentation method, which introduces a privacy division composition strategy from the view of attributes to make full use of each party’s privacy budget while meeting personalized privacy requirements for different attributes. Extensive experiments on real datasets demonstrate that our solution offers desirable data utility.

S. Guo and R. Chen—Co-corresponding authors.