Tianhao Wang, " Locally Differential Private Protocols for Frequency Estimation"

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.