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Empirical Differential Privacy

We show how to achieve differential privacy with no or reduced added noise, based on the empirical noise in the data itself. Unlike previous works on noiseless privacy, the empirical viewpoint avoids making any explicit assumptions about the random process generating the data.

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Related contextCo-authorshipCo-authorshipCo-authorshipAuthorshipAuthorshipAuthorshipTopic signalTopic signalWEmpirical Differential Privacypreprint / 2023APaul BurchardResearcherAAnthony DaoudResearcherADominic DotterrerResearcherTMachine Learning49008 worksTCryptography and Security7258 works
PaperSignal 105 links

Empirical Differential Privacy

preprint / 2023

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