Minimax and Adaptive Covariance Matrix Estimation under Differential Privacy

ArXi:2603.19703v1 Announce Type: cross The covariance matrix plays a fundamental role in the analysis of high-dimensional data. This paper studies minimax and adaptive estimation of high-dimensional bandable covariance matrices under differential privacy constraints. We propose a novel differentially private blockwise tridiagonal estimator that achieves minimax-optimal convergence rates under both the operator norm and the Frobenius norm.