Combining Differential Privacy and Secure Multiparty Computation

We consider how to perform privacy-preserving analyses on private data from different data providers and containing personal information of many different individuals. We combine differential privacy and secret sharing based secure multiparty computation in the same system to protect the privacy of both the data providers and the individuals. We have implemented a prototype of this combination and have found that the overhead of adding differential privacy to secure multiparty computation is small enough to be usable in practice.