TY - GEN
T1 - Estimating Sparse Covariance Matrix under Differential Privacy via Thresholding
AU - Wang, Di
AU - Xu, Jinhui
AU - He, Yang
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4/16
Y1 - 2019/4/16
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85065201350
U2 - 10.1109/CISS.2019.8692905
DO - 10.1109/CISS.2019.8692905
M3 - Conference contribution
AN - SCOPUS:85065201350
T3 - 2019 53rd Annual Conference on Information Sciences and Systems, CISS 2019
BT - 2019 53rd Annual Conference on Information Sciences and Systems, CISS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 53rd Annual Conference on Information Sciences and Systems, CISS 2019
Y2 - 20 March 2019 through 22 March 2019
ER -