Achieving Optimal Utility for Distributed Differential Privacy Using Secure Multiparty Computation
Eigner Fabienne, Kate Aniket, Maffei Matteo, Pampaloni Francesca, Pryvalov Ivan · IOS Press eBooks · 2015
Computing aggregate statistics about user data is of vital importance for a variety of services and systems, but this practice seriously undermines the privacy of users. Recent research efforts have focused on the development of systems for aggregating and computing statistics about user data in a distributed and privacy-preserving manner. Differential privacy has played a pivotal role in this line of research: the fundamental idea is to perturb the result of the statistics before release, which suffices to hide the contribution of individual users.