Privacy-Preserving High-dimensional Data Collection with Federated Generative Autoencoder

Business intelligence and AI services often involve the collection of copious amounts of multidimensional personal data. Since these data usually contain sensitive information of individuals, the direct collection can lead to privacy violations. Local differential privacy (LDP) is currently consider...

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Autores principales:	Jiang Xue, Zhou Xuebing, Grossklags Jens
Formato:	article
Lenguaje:	EN
Publicado:	Sciendo 2022
Materias:	high-dimensional data collection local differential privacy federated learning generative models Ethics BJ1-1725 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/4d7e219b1a474b8e87438097c4846f50
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spelling	oai:doaj.org-article:4d7e219b1a474b8e87438097c4846f502021-12-05T14:11:10ZPrivacy-Preserving High-dimensional Data Collection with Federated Generative Autoencoder2299-098410.2478/popets-2022-0024https://doaj.org/article/4d7e219b1a474b8e87438097c4846f502022-01-01T00:00:00Zhttps://doi.org/10.2478/popets-2022-0024https://doaj.org/toc/2299-0984Business intelligence and AI services often involve the collection of copious amounts of multidimensional personal data. Since these data usually contain sensitive information of individuals, the direct collection can lead to privacy violations. Local differential privacy (LDP) is currently considered a state-ofthe-art solution for privacy-preserving data collection. However, existing LDP algorithms are not applicable to high-dimensional data; not only because of the increase in computation and communication cost, but also poor data utility.Jiang XueZhou XuebingGrossklags JensSciendoarticlehigh-dimensional data collectionlocal differential privacyfederated learninggenerative modelsEthicsBJ1-1725Electronic computers. Computer scienceQA75.5-76.95ENProceedings on Privacy Enhancing Technologies, Vol 2022, Iss 1, Pp 481-500 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	high-dimensional data collection local differential privacy federated learning generative models Ethics BJ1-1725 Electronic computers. Computer science QA75.5-76.95
spellingShingle	high-dimensional data collection local differential privacy federated learning generative models Ethics BJ1-1725 Electronic computers. Computer science QA75.5-76.95 Jiang Xue Zhou Xuebing Grossklags Jens Privacy-Preserving High-dimensional Data Collection with Federated Generative Autoencoder
description	Business intelligence and AI services often involve the collection of copious amounts of multidimensional personal data. Since these data usually contain sensitive information of individuals, the direct collection can lead to privacy violations. Local differential privacy (LDP) is currently considered a state-ofthe-art solution for privacy-preserving data collection. However, existing LDP algorithms are not applicable to high-dimensional data; not only because of the increase in computation and communication cost, but also poor data utility.
format	article
author	Jiang Xue Zhou Xuebing Grossklags Jens
author_facet	Jiang Xue Zhou Xuebing Grossklags Jens
author_sort	Jiang Xue
title	Privacy-Preserving High-dimensional Data Collection with Federated Generative Autoencoder
title_short	Privacy-Preserving High-dimensional Data Collection with Federated Generative Autoencoder
title_full	Privacy-Preserving High-dimensional Data Collection with Federated Generative Autoencoder
title_fullStr	Privacy-Preserving High-dimensional Data Collection with Federated Generative Autoencoder
title_full_unstemmed	Privacy-Preserving High-dimensional Data Collection with Federated Generative Autoencoder
title_sort	privacy-preserving high-dimensional data collection with federated generative autoencoder
publisher	Sciendo
publishDate	2022
url	https://doaj.org/article/4d7e219b1a474b8e87438097c4846f50
work_keys_str_mv	AT jiangxue privacypreservinghighdimensionaldatacollectionwithfederatedgenerativeautoencoder AT zhouxuebing privacypreservinghighdimensionaldatacollectionwithfederatedgenerativeautoencoder AT grossklagsjens privacypreservinghighdimensionaldatacollectionwithfederatedgenerativeautoencoder
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Privacy-Preserving High-dimensional Data Collection with Federated Generative Autoencoder

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