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The Discrete Gaussian Expectation Maximization (Gradient) Algorithm for Differential Privacy

In this paper, we give a modified gradient EM algorithm; it can protect the privacy of sensitive data by adding discrete Gaussian mechanism noise. Specifically, it makes the high-dimensional data easier to process mainly by scaling, truncating, noise multiplication, and smoothing steps on the data....

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Detalles Bibliográficos
Autor principal: Wu, Weisan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8739528/
https://www.ncbi.nlm.nih.gov/pubmed/35003248
http://dx.doi.org/10.1155/2021/7962489
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author Wu, Weisan
author_facet Wu, Weisan
author_sort Wu, Weisan
collection PubMed
description In this paper, we give a modified gradient EM algorithm; it can protect the privacy of sensitive data by adding discrete Gaussian mechanism noise. Specifically, it makes the high-dimensional data easier to process mainly by scaling, truncating, noise multiplication, and smoothing steps on the data. Since the variance of discrete Gaussian is smaller than that of the continuous Gaussian, the difference privacy of data can be guaranteed more effectively by adding the noise of the discrete Gaussian mechanism. Finally, the standard gradient EM algorithm, clipped algorithm, and our algorithm (DG-EM) are compared with the GMM model. The experiments show that our algorithm can effectively protect high-dimensional sensitive data.
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spelling pubmed-87395282022-01-08 The Discrete Gaussian Expectation Maximization (Gradient) Algorithm for Differential Privacy Wu, Weisan Comput Intell Neurosci Research Article In this paper, we give a modified gradient EM algorithm; it can protect the privacy of sensitive data by adding discrete Gaussian mechanism noise. Specifically, it makes the high-dimensional data easier to process mainly by scaling, truncating, noise multiplication, and smoothing steps on the data. Since the variance of discrete Gaussian is smaller than that of the continuous Gaussian, the difference privacy of data can be guaranteed more effectively by adding the noise of the discrete Gaussian mechanism. Finally, the standard gradient EM algorithm, clipped algorithm, and our algorithm (DG-EM) are compared with the GMM model. The experiments show that our algorithm can effectively protect high-dimensional sensitive data. Hindawi 2021-12-30 /pmc/articles/PMC8739528/ /pubmed/35003248 http://dx.doi.org/10.1155/2021/7962489 Text en Copyright © 2021 Weisan Wu. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wu, Weisan
The Discrete Gaussian Expectation Maximization (Gradient) Algorithm for Differential Privacy
title The Discrete Gaussian Expectation Maximization (Gradient) Algorithm for Differential Privacy
title_full The Discrete Gaussian Expectation Maximization (Gradient) Algorithm for Differential Privacy
title_fullStr The Discrete Gaussian Expectation Maximization (Gradient) Algorithm for Differential Privacy
title_full_unstemmed The Discrete Gaussian Expectation Maximization (Gradient) Algorithm for Differential Privacy
title_short The Discrete Gaussian Expectation Maximization (Gradient) Algorithm for Differential Privacy
title_sort discrete gaussian expectation maximization (gradient) algorithm for differential privacy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8739528/
https://www.ncbi.nlm.nih.gov/pubmed/35003248
http://dx.doi.org/10.1155/2021/7962489
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