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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| Format: | article |
| Langue: | EN |
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Nature Portfolio
2020
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| Accès en ligne: | https://doaj.org/article/673d692598a3489a96ad65da8d9b8a30 |
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