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      Heap Bucketization Anonymity—An Efficient Privacy-Preserving Data Publishing Model for Multiple Sensitive Attributes

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          t-Closeness: Privacy Beyond k-Anonymity and l-Diversity

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            Protecting respondents identities in microdata release

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              ACHIEVING k-ANONYMITY PRIVACY PROTECTION USING GENERALIZATION AND SUPPRESSION

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                Author and article information

                Contributors
                (View ORCID Profile)
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                Journal
                IEEE Access
                IEEE Access
                Institute of Electrical and Electronics Engineers (IEEE)
                2169-3536
                2022
                2022
                : 10
                : 28773-28791
                Affiliations
                [1 ]Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
                [2 ]Department of Data Science and Business Systems, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
                [3 ]Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia
                [4 ]Al-Nahrain Nanorenewable Energy Research Center, Al-Nahrain University, Baghdad, Iraq
                [5 ]Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
                Article
                10.1109/ACCESS.2022.3158312
                e6a9824b-ba0c-48c8-a188-4028ae382917
                © 2022

                https://creativecommons.org/licenses/by/4.0/legalcode

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