Analytics on Anonymity for Privacy Retention in Smart Health Data

Advancements in smart technology, wearable and mobile devices, and Internet of Things, have made smart health an integral part of modern living to better individual healthcare and well-being. By enhancing self-monitoring, data collection and sharing among users and service providers, smart health ca...

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Autores principales:	Sevgi Arca, Rattikorn Hewett
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	health data anonymity analytics privacy in smart health data anonymization Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/4bc7174a9e3d4da7928ab28b1c94ab98
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spelling	oai:doaj.org-article:4bc7174a9e3d4da7928ab28b1c94ab982021-11-25T17:39:41ZAnalytics on Anonymity for Privacy Retention in Smart Health Data10.3390/fi131102741999-5903https://doaj.org/article/4bc7174a9e3d4da7928ab28b1c94ab982021-10-01T00:00:00Zhttps://www.mdpi.com/1999-5903/13/11/274https://doaj.org/toc/1999-5903Advancements in smart technology, wearable and mobile devices, and Internet of Things, have made smart health an integral part of modern living to better individual healthcare and well-being. By enhancing self-monitoring, data collection and sharing among users and service providers, smart health can increase healthy lifestyles, timely treatments, and save lives. However, as health data become larger and more accessible to multiple parties, they become vulnerable to privacy attacks. One way to safeguard privacy is to increase users’ anonymity as anonymity increases indistinguishability making it harder for re-identification. Still the challenge is not only to preserve data privacy but also to ensure that the shared data are sufficiently informative to be useful. Our research studies health data analytics focusing on anonymity for privacy protection. This paper presents a multi-faceted analytical approach to (1) identifying attributes susceptible to information leakages by using entropy-based measure to analyze information loss, (2) anonymizing the data by generalization using attribute hierarchies, and (3) balancing between anonymity and informativeness by our anonymization technique that produces anonymized data satisfying a given anonymity requirement while optimizing data retention. Our anonymization technique is an automated Artificial Intelligent search based on two simple heuristics. The paper describes and illustrates the detailed approach and analytics including pre and post anonymization analytics. Experiments on published data are performed on the anonymization technique. Results, compared with other similar techniques, show that our anonymization technique gives the most effective data sharing solution, with respect to computational cost and balancing between anonymity and data retention.Sevgi ArcaRattikorn HewettMDPI AGarticlehealth data anonymity analyticsprivacy in smart healthdata anonymizationInformation technologyT58.5-58.64ENFuture Internet, Vol 13, Iss 274, p 274 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	health data anonymity analytics privacy in smart health data anonymization Information technology T58.5-58.64
spellingShingle	health data anonymity analytics privacy in smart health data anonymization Information technology T58.5-58.64 Sevgi Arca Rattikorn Hewett Analytics on Anonymity for Privacy Retention in Smart Health Data
description	Advancements in smart technology, wearable and mobile devices, and Internet of Things, have made smart health an integral part of modern living to better individual healthcare and well-being. By enhancing self-monitoring, data collection and sharing among users and service providers, smart health can increase healthy lifestyles, timely treatments, and save lives. However, as health data become larger and more accessible to multiple parties, they become vulnerable to privacy attacks. One way to safeguard privacy is to increase users’ anonymity as anonymity increases indistinguishability making it harder for re-identification. Still the challenge is not only to preserve data privacy but also to ensure that the shared data are sufficiently informative to be useful. Our research studies health data analytics focusing on anonymity for privacy protection. This paper presents a multi-faceted analytical approach to (1) identifying attributes susceptible to information leakages by using entropy-based measure to analyze information loss, (2) anonymizing the data by generalization using attribute hierarchies, and (3) balancing between anonymity and informativeness by our anonymization technique that produces anonymized data satisfying a given anonymity requirement while optimizing data retention. Our anonymization technique is an automated Artificial Intelligent search based on two simple heuristics. The paper describes and illustrates the detailed approach and analytics including pre and post anonymization analytics. Experiments on published data are performed on the anonymization technique. Results, compared with other similar techniques, show that our anonymization technique gives the most effective data sharing solution, with respect to computational cost and balancing between anonymity and data retention.
format	article
author	Sevgi Arca Rattikorn Hewett
author_facet	Sevgi Arca Rattikorn Hewett
author_sort	Sevgi Arca
title	Analytics on Anonymity for Privacy Retention in Smart Health Data
title_short	Analytics on Anonymity for Privacy Retention in Smart Health Data
title_full	Analytics on Anonymity for Privacy Retention in Smart Health Data
title_fullStr	Analytics on Anonymity for Privacy Retention in Smart Health Data
title_full_unstemmed	Analytics on Anonymity for Privacy Retention in Smart Health Data
title_sort	analytics on anonymity for privacy retention in smart health data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4bc7174a9e3d4da7928ab28b1c94ab98
work_keys_str_mv	AT sevgiarca analyticsonanonymityforprivacyretentioninsmarthealthdata AT rattikornhewett analyticsonanonymityforprivacyretentioninsmarthealthdata
_version_	1718412101703172096

Analytics on Anonymity for Privacy Retention in Smart Health Data

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