Estimating Sparse Covariance Matrix under Differential Privacy via Thresholding

Di Wang, Jinhui Xu, Yang He

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

1 Scopus citations


In this paper, we study the problem of estimating the covariance matrix under differential privacy, where the underlying covariance matrix is assumed to be sparse and of high dimensions. We propose a new method, called DP-Thresholding, to achieve a non-trivial l2-norm based error bound, which is significantly better than the existing ones from adding noise directly to the empirical covariance matrix. Experiments on the synthetic datasets show consistent results with our theoretical claims.
Original languageEnglish (US)
Title of host publication2019 53rd Annual Conference on Information Sciences and Systems, CISS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781728111513
StatePublished - Apr 16 2019
Externally publishedYes

Bibliographical note

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


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