Locally Differential Private Protocols for Frequency Estimation
Tianhao Wang - Purdue University
Oct 18, 2017Size: 106.3MB
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AbstractProtocols 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.
About the SpeakerTiahhao Wang is a Ph.D. candidate at Purdue University working with Professor Ninghui Li. His research focuses on the practical aspects of privacy and security. Tiahhao’s research has been published at top tier security venues such as USENIX and CCS, and his Erdos number is 3. His current research projects include local differential privacy and electronic voting.
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