Efficiently Supporting Online Privacy-Preserving Data Publishing in a Distributed Computing Environment

There has recently been an increasing need for the collection and sharing of microdata containing information regarding an individual entity. Because microdata typically contain sensitive information on an individual, releasing it directly for public use may violate existing privacy requirements. Th...

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Autor principal:	Jong Wook Kim
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	privacy-preserving data publishing <i>k</i>-anonymity distributed query processing Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/3a8ed715e88b46d18d7276989ca88e93
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spelling	oai:doaj.org-article:3a8ed715e88b46d18d7276989ca88e932021-11-25T16:37:02ZEfficiently Supporting Online Privacy-Preserving Data Publishing in a Distributed Computing Environment10.3390/app1122107402076-3417https://doaj.org/article/3a8ed715e88b46d18d7276989ca88e932021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10740https://doaj.org/toc/2076-3417There has recently been an increasing need for the collection and sharing of microdata containing information regarding an individual entity. Because microdata typically contain sensitive information on an individual, releasing it directly for public use may violate existing privacy requirements. Thus, extensive studies have been conducted on privacy-preserving data publishing (PPDP), which ensures that any microdata released satisfy the privacy policy requirements. Most existing privacy-preserving data publishing algorithms consider a scenario in which a data publisher, receiving a request for the release of data containing personal information, anonymizes the data prior to publishing—a process that is usually conducted offline. However, with the increasing demand for the sharing of data among various parties, it is more desirable to integrate the data anonymization functionality into existing systems that are capable of supporting online query processing. Thus, we developed a novel scheme that is able to efficiently anonymize the query results on the fly, and thus support efficient online privacy-preserving data publishing. In particular, given a user’s query, the proposed approach effectively estimates the generalization level of each quasi-identifier attribute, thereby achieving the <i>k</i>-anonymity property in the query result datasets based on the statistical information without applying <i>k</i>-anonymity on all actual datasets, which is a costly procedure. The experiment results show that, through the proposed method, significant gains in processing time can be achieved.Jong Wook KimMDPI AGarticleprivacy-preserving data publishing<i>k</i>-anonymitydistributed query processingTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10740, p 10740 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	privacy-preserving data publishing <i>k</i>-anonymity distributed query processing Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	privacy-preserving data publishing <i>k</i>-anonymity distributed query processing Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Jong Wook Kim Efficiently Supporting Online Privacy-Preserving Data Publishing in a Distributed Computing Environment
description	There has recently been an increasing need for the collection and sharing of microdata containing information regarding an individual entity. Because microdata typically contain sensitive information on an individual, releasing it directly for public use may violate existing privacy requirements. Thus, extensive studies have been conducted on privacy-preserving data publishing (PPDP), which ensures that any microdata released satisfy the privacy policy requirements. Most existing privacy-preserving data publishing algorithms consider a scenario in which a data publisher, receiving a request for the release of data containing personal information, anonymizes the data prior to publishing—a process that is usually conducted offline. However, with the increasing demand for the sharing of data among various parties, it is more desirable to integrate the data anonymization functionality into existing systems that are capable of supporting online query processing. Thus, we developed a novel scheme that is able to efficiently anonymize the query results on the fly, and thus support efficient online privacy-preserving data publishing. In particular, given a user’s query, the proposed approach effectively estimates the generalization level of each quasi-identifier attribute, thereby achieving the <i>k</i>-anonymity property in the query result datasets based on the statistical information without applying <i>k</i>-anonymity on all actual datasets, which is a costly procedure. The experiment results show that, through the proposed method, significant gains in processing time can be achieved.
format	article
author	Jong Wook Kim
author_facet	Jong Wook Kim
author_sort	Jong Wook Kim
title	Efficiently Supporting Online Privacy-Preserving Data Publishing in a Distributed Computing Environment
title_short	Efficiently Supporting Online Privacy-Preserving Data Publishing in a Distributed Computing Environment
title_full	Efficiently Supporting Online Privacy-Preserving Data Publishing in a Distributed Computing Environment
title_fullStr	Efficiently Supporting Online Privacy-Preserving Data Publishing in a Distributed Computing Environment
title_full_unstemmed	Efficiently Supporting Online Privacy-Preserving Data Publishing in a Distributed Computing Environment
title_sort	efficiently supporting online privacy-preserving data publishing in a distributed computing environment
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3a8ed715e88b46d18d7276989ca88e93
work_keys_str_mv	AT jongwookkim efficientlysupportingonlineprivacypreservingdatapublishinginadistributedcomputingenvironment
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Efficiently Supporting Online Privacy-Preserving Data Publishing in a Distributed Computing Environment

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