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Deconvoluting kernel density estimation and regression for locally differentially private data
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 ensure priv...
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Nature Publishing Group UK
2020
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7721740/ https://www.ncbi.nlm.nih.gov/pubmed/33288799 http://dx.doi.org/10.1038/s41598-020-78323-0 |