A privacy-preserving test based on Laplace-perturbed sample eigenvalues is asymptotically distribution-free and detects n^{-1/2} local alternatives to Sigma = I.
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Testing for large-dimensional covariance matrix under differential privacy
A privacy-preserving test based on Laplace-perturbed sample eigenvalues is asymptotically distribution-free and detects n^{-1/2} local alternatives to Sigma = I.