Differentially Private Sparse Covariance Matrix Estimation under Lower-Bounded Moment Assumption

Huimin Li and Jinru Wang ()
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Huimin Li: Department of Mathematics, Beijing University of Technology, Beijing 100124, China
Jinru Wang: Department of Mathematics, Beijing University of Technology, Beijing 100124, China

Mathematics, 2023, vol. 11, issue 17, 1-16

Abstract: This paper investigates the problem of sparse covariance matrix estimation while the sampling set contains sensitive information, and both the differentially private algorithm and locally differentially private algorithm are adopted to preserve privacy. It is worth noting that the requirement of the distribution assumption in our work is only the existing bounded 4 + ε ( ε > 0 ) moment. Meanwhile, we reduce the error bounds by modifying the threshold of the existing differentially private algorithms. Finally, the numerical simulations and results from a real data application are presented to support our theoretical claims.

Keywords: differential privacy; lower-bounded moment; sparse covariance matrix; probability estimation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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