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 |
| _version_ | 1818431016808742912 |
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| author | Farhad Farokhi |
| author_facet | Farhad Farokhi |
| author_sort | Farhad Farokhi |
| collection | DOAJ |
| description | 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 ensure privacy. In fact, the density of privacy-preserving data (no matter how many samples we gather) is always flatter in comparison with the density function of the original data points due to convolution with privacy-preserving noise density function. The effect is especially more pronounced when using slow-decaying privacy-preserving noises, such as the Laplace noise. This can result in under/over-estimation of the heavy-hitters. This is an important challenge facing social scientists due to the use of differential privacy in the 2020 Census in the United States. In this paper, we develop density estimation methods using smoothing kernels. We use the framework of deconvoluting kernel density estimators to remove the effect of privacy-preserving noise. This approach also allows us to adapt the results from non-parametric regression with errors-in-variables to develop regression models based on locally differentially private data. We demonstrate the performance of the developed methods on financial and demographic datasets. |
| first_indexed | 2024-12-14T15:42:36Z |
| format | Article |
| id | doaj.art-673d692598a3489a96ad65da8d9b8a30 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-14T15:42:36Z |
| publishDate | 2020-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-673d692598a3489a96ad65da8d9b8a302022-12-21T22:55:35ZengNature PortfolioScientific Reports2045-23222020-12-0110111110.1038/s41598-020-78323-0Deconvoluting kernel density estimation and regression for locally differentially private dataFarhad Farokhi0Department of Electrical and Electronic Engineering, The University of MelbourneAbstract 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 privacy. In fact, the density of privacy-preserving data (no matter how many samples we gather) is always flatter in comparison with the density function of the original data points due to convolution with privacy-preserving noise density function. The effect is especially more pronounced when using slow-decaying privacy-preserving noises, such as the Laplace noise. This can result in under/over-estimation of the heavy-hitters. This is an important challenge facing social scientists due to the use of differential privacy in the 2020 Census in the United States. In this paper, we develop density estimation methods using smoothing kernels. We use the framework of deconvoluting kernel density estimators to remove the effect of privacy-preserving noise. This approach also allows us to adapt the results from non-parametric regression with errors-in-variables to develop regression models based on locally differentially private data. We demonstrate the performance of the developed methods on financial and demographic datasets.https://doi.org/10.1038/s41598-020-78323-0 |
| spellingShingle | Farhad Farokhi Deconvoluting kernel density estimation and regression for locally differentially private data Scientific Reports |
| title | Deconvoluting kernel density estimation and regression for locally differentially private data |
| title_full | Deconvoluting kernel density estimation and regression for locally differentially private data |
| title_fullStr | Deconvoluting kernel density estimation and regression for locally differentially private data |
| title_full_unstemmed | Deconvoluting kernel density estimation and regression for locally differentially private data |
| title_short | Deconvoluting kernel density estimation and regression for locally differentially private data |
| title_sort | deconvoluting kernel density estimation and regression for locally differentially private data |
| url | https://doi.org/10.1038/s41598-020-78323-0 |
| work_keys_str_mv | AT farhadfarokhi deconvolutingkerneldensityestimationandregressionforlocallydifferentiallyprivatedata |