Web: http://arxiv.org/abs/2106.05042

June 24, 2022, 1:11 a.m. | Margarita Vinaroz, Mohammad-Amin Charusaie, Frederik Harder, Kamil Adamczewski, Mijung Park

stat.ML updates on arXiv.org arxiv.org

Kernel mean embedding is a useful tool to represent and compare probability
measures. Despite its usefulness, kernel mean embedding considers
infinite-dimensional features, which are challenging to handle in the context
of differentially private data generation. A recent work proposes to
approximate the kernel mean embedding of data distribution using
finite-dimensional random features, which yields analytically tractable
sensitivity. However, the number of required random features is excessively
high, often ten thousand to a hundred thousand, which worsens the
privacy-accuracy trade-off. To …

arxiv data features generation lg polynomial private data

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