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A Comparative Study of Data Anonymization Techniques

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dc.citedby20
dc.contributor.authorMurthy S.en_US
dc.contributor.authorAbu Bakar A.en_US
dc.contributor.authorAbdul Rahim F.en_US
dc.contributor.authorRamli R.en_US
dc.contributor.authorid57215129870en_US
dc.contributor.authorid35178991300en_US
dc.contributor.authorid57350579500en_US
dc.contributor.authorid57191413657en_US
dc.date.accessioned2023-05-29T07:25:38Z
dc.date.available2023-05-29T07:25:38Z
dc.date.issued2019
dc.descriptionBig data; Data privacy; Anonymization; Comparative studies; Data anonymization; Digital era; Personally identifiable information; Privacy preservation; Privacy risks; security; Network securityen_US
dc.description.abstractIn today's digital era, it is a very common practice for organizations to collect data from individual users. The collected data is then stored in multiple databases which contain personally identifiable information (PII). This may lead to a major source of privacy risk for the database. Various privacy preservation techniques have been proposed such as perturbation, anonymization and cryptographic. In this study, five anonymization techniques are compared using the same dataset. In addition to that, this study reviews the strengths and weaknesses of the different technique. In the evaluation of efficiency, suppression is found as the most efficient while swapping is in the last place. It is also revealed that swapping is the most resource-consuming technique while suppressing being less resource consuming. � 2019 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8819477
dc.identifier.doi10.1109/BigDataSecurity-HPSC-IDS.2019.00063
dc.identifier.epage309
dc.identifier.scopus2-s2.0-85072765291
dc.identifier.spage306
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85072765291&doi=10.1109%2fBigDataSecurity-HPSC-IDS.2019.00063&partnerID=40&md5=0cff5e2b3819a6818d170d011b623056
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24664
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleProceedings - 5th IEEE International Conference on Big Data Security on Cloud, BigDataSecurity 2019, 5th IEEE International Conference on High Performance and Smart Computing, HPSC 2019 and 4th IEEE International Conference on Intelligent Data and Security, IDS 2019
dc.titleA Comparative Study of Data Anonymization Techniquesen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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