Paper 2025/851

V$\epsilon$rity: Verifiable Local Differential Privacy

Abstract

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, $\textit{V}\epsilon\textit{rity-}{\textit{Auth}}$, addresses scenarios where the users report inputs with a ground truth available to a third party. The second solution, $\textit{V}\epsilon\textit{rity}$, 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.