Locally private Gaussian estimation

Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Zhiwei Steven Wu

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations

Abstract

We study a basic private estimation problem: each of n users draws a single i.i.d. sample from an unknown Gaussian distribution N(µ, s2), and the goal is to estimate µ while guaranteeing local differential privacy for each user. As minimizing the number of rounds of interaction is important in the local setting, we provide adaptive two-round solutions and nonadaptive one-round solutions to this problem. We match these upper bounds with an information-theoretic lower bound showing that our accuracy guarantees are tight up to logarithmic factors for all sequentially interactive locally private protocols.

Original languageEnglish (US)
JournalAdvances in Neural Information Processing Systems
Volume32
StatePublished - 2019
Event33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019 - Vancouver, Canada
Duration: Dec 8 2019Dec 14 2019

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