Construct · 1 fields · 1 citations
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In privacy-enhancing computation, smooth sensitivity refers to a smooth upper bound on a function's local sensitivity at the actual dataset, letting noise be calibrated to the data while preserving differential privacy. This meaning comes from Nissim (2007) in Proceedings of the thirty-ninth annual ACM symposium on Theory of computing.[1]
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