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Differentially Private High Dimensional Sparse Covariance Matrix Estimation
[article]
2019
arXiv
pre-print
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 ℓ_2-norm based error bound, which is significantly better than the existing ones from adding noise directly to the empirical covariance matrix. We also extend the ℓ_2-norm based error bound to a general ℓ_w-norm based one for any 1≤ w≤∞,
arXiv:1901.06413v2
fatcat:g3vyqzkyrvh5bbaqwssuthxffu