Hermite Polynomial Features for Private Data Generation. (arXiv:2106.05042v4 [cs.LG] UPDATED)

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