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Efficient Algorithms for Locally Private Estimation with Optimal Accuracy Guarantees

Vitaly Feldman, Apple ML Research
E18-304

Abstract: Locally Differentially Private (LDP) reports are commonly used for collection of statistics and machine learning in the federated setting with an untrusted server. We study the efficiency of two basic tasks, frequency estimation and vector mean estimation, using LDP reports. Existing algorithms for these problems that achieve the lowest error are neither communication nor computation efficient in the high-dimensional regime. In this talk I’ll describe new efficient LDP algorithms for these tasks that achieve the optimal error (up to…

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