Optimizing Locally Differentially Private Protocols
This work addresses the need for more efficient privacy-preserving data collection in scenarios like real-world deployments (e.g., Google's RAPPOR), though it is incremental as it builds on prior LDP protocols.
The paper tackles the problem of improving utility in Local Differential Privacy (LDP) protocols, which protect user privacy without a trusted third party, by introducing a framework that generalizes existing methods and yields two new protocols (Optimized Unary Encoding and Optimized Local Hashing) with better accuracy, as demonstrated through experiments.
Protocols satisfying Local Differential Privacy (LDP) enable parties to collect aggregate information about a population while protecting each user's privacy, without relying on a trusted third party. LDP protocols (such as Google's RAPPOR) have been deployed in real-world scenarios. In these protocols, a user encodes his private information and perturbs the encoded value locally before sending it to an aggregator, who combines values that users contribute to infer statistics about the population. In this paper, we introduce a framework that generalizes several LDP protocols proposed in the literature. Our framework yields a simple and fast aggregation algorithm, whose accuracy can be precisely analyzed. Our in-depth analysis enables us to choose optimal parameters, resulting in two new protocols (i.e., Optimized Unary Encoding and Optimized Local Hashing) that provide better utility than protocols previously proposed. We present precise conditions for when each proposed protocol should be used, and perform experiments that demonstrate the advantage of our proposed protocols.