Multiparty Differential Privacy via Aggregation of Locally Trained Classifiers
Manas A. Pathak, Shantanu D. Rane, Bhiksha Raj · 2010
As increasing amounts of sensitive personal information finds its way into data repositories, it is important to develop analysis mechanisms that can derive ag-gregate information from these repositories without revealing information about individual data instances. Though the differential privacy model provides a frame-work to analyze such mechanisms for databases belonging to a single party, this framework has not yet been considered in a multi-party setting. In this paper, we propose a privacy-preserving protocol for composing a differentially private ag-gregate classifier using classifiers trained locally by separate mutually untrusting parties. The protocol allows these parties to interact with an untrusted curator to construct additive shares of a perturbed aggregate classifier. We also present a detailed theoretical analysis containing a proof of differential privacy of the per-turbed aggregate classifier and a bound on the excess risk introduced by the per-turbation. We verify the bound with an experimental evaluation on a real dataset. 1