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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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| Publicado no: | Sci Rep |
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| Autor principal: | |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Nature Publishing Group UK
2020
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7721740/ https://ncbi.nlm.nih.gov/pubmed/33288799 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41598-020-78323-0 |
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