Vεrity: Verifiable Local Differential Privacy

Local differential privacy (LDP) enables individuals to report sensitive data while preserving privacy. Unfortunately, LDP mechanisms are vulnerable to poisoning attacks, where adversaries controlling a fraction of the reporting users can significantly distort the aggregate output–much more so than in a non-private solution where the inputs are reported directly. In this paper, we present two novel solutions that prevent poisoning attacks under LDP while preserving its privacy guarantees.
Our first solution, Vεrity-Auth, addresses scenarios where the users report inputs with a ground truth available to a third party. The second solution, Vεrity, tackles the more challenging case in which the users locally generate their input and there is no ground truth which can be used to bootstrap verifiable randomness generation.