Differentially private density estimation with skew-normal mixtures model

Abstract The protection of private data is a hot research issue in the era of big data. Differential privacy is a strong privacy guarantees in data analysis. In this paper, we propose DP-MSNM, a parametric density estimation algorithm using multivariate skew-normal mixtures (MSNM) model to different...

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Autor principal:	Weisan Wu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/38a81501649e481ca7c43f94c250503a
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spelling	oai:doaj.org-article:38a81501649e481ca7c43f94c250503a2021-12-02T14:49:11ZDifferentially private density estimation with skew-normal mixtures model10.1038/s41598-021-90276-62045-2322https://doaj.org/article/38a81501649e481ca7c43f94c250503a2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90276-6https://doaj.org/toc/2045-2322Abstract The protection of private data is a hot research issue in the era of big data. Differential privacy is a strong privacy guarantees in data analysis. In this paper, we propose DP-MSNM, a parametric density estimation algorithm using multivariate skew-normal mixtures (MSNM) model to differential privacy. MSNM can solve the asymmetric problem of data sets, and it is could approximate any distribution through expectation–maximization (EM) algorithm. In this model, we add two extra steps on the estimated parameters in the M step of each iteration. The first step is adding calibrated noise to the estimated parameters based on Laplacian mechanism. The second step is post-processes those noisy parameters to ensure their intrinsic characteristics based on the theory of vector normalize and positive semi definition matrix. Extensive experiments using both real data sets evaluate the performance of DP-MSNM, and demonstrate that the proposed method outperforms DPGMM.Weisan WuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Weisan Wu Differentially private density estimation with skew-normal mixtures model
description	Abstract The protection of private data is a hot research issue in the era of big data. Differential privacy is a strong privacy guarantees in data analysis. In this paper, we propose DP-MSNM, a parametric density estimation algorithm using multivariate skew-normal mixtures (MSNM) model to differential privacy. MSNM can solve the asymmetric problem of data sets, and it is could approximate any distribution through expectation–maximization (EM) algorithm. In this model, we add two extra steps on the estimated parameters in the M step of each iteration. The first step is adding calibrated noise to the estimated parameters based on Laplacian mechanism. The second step is post-processes those noisy parameters to ensure their intrinsic characteristics based on the theory of vector normalize and positive semi definition matrix. Extensive experiments using both real data sets evaluate the performance of DP-MSNM, and demonstrate that the proposed method outperforms DPGMM.
format	article
author	Weisan Wu
author_facet	Weisan Wu
author_sort	Weisan Wu
title	Differentially private density estimation with skew-normal mixtures model
title_short	Differentially private density estimation with skew-normal mixtures model
title_full	Differentially private density estimation with skew-normal mixtures model
title_fullStr	Differentially private density estimation with skew-normal mixtures model
title_full_unstemmed	Differentially private density estimation with skew-normal mixtures model
title_sort	differentially private density estimation with skew-normal mixtures model
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/38a81501649e481ca7c43f94c250503a
work_keys_str_mv	AT weisanwu differentiallyprivatedensityestimationwithskewnormalmixturesmodel
_version_	1718389503552389120

Differentially private density estimation with skew-normal mixtures model

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