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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Autor principal:	Farhad Farokhi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/673d692598a3489a96ad65da8d9b8a30
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spelling	oai:doaj.org-article:673d692598a3489a96ad65da8d9b8a302021-12-02T12:33:46ZDeconvoluting kernel density estimation and regression for locally differentially private data10.1038/s41598-020-78323-02045-2322https://doaj.org/article/673d692598a3489a96ad65da8d9b8a302020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78323-0https://doaj.org/toc/2045-2322Abstract 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.Farhad FarokhiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Farhad Farokhi Deconvoluting kernel density estimation and regression for locally differentially private data
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.
format	article
author	Farhad Farokhi
author_facet	Farhad Farokhi
author_sort	Farhad Farokhi
title	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_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_sort	deconvoluting kernel density estimation and regression for locally differentially private data
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/673d692598a3489a96ad65da8d9b8a30
work_keys_str_mv	AT farhadfarokhi deconvolutingkerneldensityestimationandregressionforlocallydifferentiallyprivatedata
_version_	1718393862338117632

Deconvoluting kernel density estimation and regression for locally differentially private data

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