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 |
| Language: | English |
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Nature Portfolio
2020-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-020-78323-0 |