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In this work, we give efficient algorithms for privately estimating a Gaussian distribution in both pure and approximate differential privacy (DP) models with optimal dependence on the dimension in the sample complexity.
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John W. Tukey · 1960
Earlier work this paper cites.
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A general class of coefficients of divergence of one distribution from another
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Information-type measures of difference of probability distributions and indirect observation
I. CSISZAR · 1967
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