Differentially private sparse inverse covariance estimation

Di Wang, Mengdi Huai, Jinhui Xu

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

6 Scopus citations


In this paper, we present the first results on the sparse inverse covariance estimation problem under the differential privacy model. We first gave an ϵ-differentially private algorithm using output perturbation strategy, which is based on the sensitivity of the optimization problem and the Wishart mechanism. To further improve this result, we then introduce a general covariance perturbation method to achieve both ϵ-differential privacy and (ϵ, δ)-differential privacy. For ϵ-differential privacy, we analyze the performance of Laplacian and Wishart mechanisms, and for (ϵ, δ)-differential privacy, we examine the performance of Gaussian and Wishart mechanisms. Experiments on both synthetic and benchmark datasets confirm our theoretical analysis.
Original languageEnglish (US)
Title of host publication2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Print)9781728112954
StatePublished - Feb 20 2019
Externally publishedYes


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