Deconvoluting kernel density estimation and regression for locally differentially private data

Abstract Local differential privacy has become the gold-standard of privacy literature for gathering or releasing sensitive individual data points in a privacy-preserving manner. However, locally differential data can twist the probability density of the data because of the additive noise used to en...

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Autor principal: Farhad Farokhi
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2020
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Acceso en línea:https://doaj.org/article/673d692598a3489a96ad65da8d9b8a30
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