Composable and versatile privacy via truncated CDP.

STOC '18: Symposium on Theory of Computing Los Angeles CA USA June, 2018(2018)

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摘要
We propose truncated concentrated differential privacy (tCDP), a refinement of differential privacy and of concentrated differential privacy. This new definition provides robust and efficient composition guarantees, supports powerful algorithmic techniques such as privacy amplification via sub-sampling, and enables more accurate statistical analyses. In particular, we show a central task for which the new definition enables exponential accuracy improvement.
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关键词
differential privacy,algorithmic stability,subsampling
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