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.
|Title of host publication
|2019 53rd Annual Conference on Information Sciences and Systems, CISS 2019
|Institute of Electrical and Electronics Engineers Inc.
|Published - Apr 16 2019