Differentially private high dimensional sparse covariance matrix estimation

Di Wang; Jinhui Xu

doi:10.1016/j.tcs.2021.03.001

ScienceGate Book Chapters

JOURNAL ARTICLE

Differentially private high dimensional sparse covariance matrix estimation

Di Wang Jinhui Xu

Year: 2021 Journal: Theoretical Computer Science Vol: 865 Pages: 119-130 Publisher: Elsevier BV

DOI: 10.1016/j.tcs.2021.03.001

Get Full-Text PDF Get Analytical Report

Keywords:

Covariance Estimation of covariance matrices Differential privacy Upper and lower bounds Logarithm Mathematics Covariance matrix Matrix norm Dimension (graph theory) Applied mathematics Estimator Norm (philosophy) Matrix (chemical analysis) Asymptotically optimal algorithm Law of total covariance Covariance function Mathematical optimization Algorithm Covariance intersection Combinatorics Eigenvalues and eigenvectors Statistics Mathematical analysis

Metrics

Cited By

0.28

FWCI (Field Weighted Citation Impact)

Refs

0.61

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Privacy-Preserving Technologies in Data

Physical Sciences → Computer Science → Artificial Intelligence

Random Matrices and Applications

Physical Sciences → Mathematics → Statistics and Probability

Cryptography and Data Security

Physical Sciences → Computer Science → Artificial Intelligence

Differentially private high dimensional sparse covariance matrix estimation

Metrics

Citation History

Topics

Related Documents

DIFFERENTIALLY PRIVATE SPARSE INVERSE COVARIANCE ESTIMATION

Lower Bound of Locally Differentially Private Sparse Covariance Matrix Estimation

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

Automatic sparse estimation of the high-dimensional cross-covariance matrix

High-Dimensional Covariance Matrix Estimation