Lower bound of locally differentially private sparse covariance matrix estimation

Di Wang, Jinhui Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

In this paper, we study the sparse covariance matrix estimation problem in the local differential privacy model, and give a non-trivial lower bound on the non-interactive private minimax risk in the metric of squared spectral norm. We show that the lower bound is actually tight, as it matches a previous upper bound. Our main technique for achieving this lower bound is a general framework, called General Private Assouad Lemma, which is a considerable generalization of the previous private Assouad lemma and can be used as a general method for bounding the private minimax risk of matrix-related estimation problems.
Original languageEnglish (US)
Title of host publicationIJCAI International Joint Conference on Artificial Intelligence
PublisherInternational Joint Conferences on Artificial IntelligenceThomas.schiex@toulouse.inra.fr
Pages4788-4794
Number of pages7
ISBN (Print)9780999241141
DOIs
StatePublished - Jan 1 2019
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-15

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