Efficient Anonymization Algorithm for Multiple Sensitive Attributes
S.Srijayanthi1, T.Sethukarasi2, A.Thilagavathy3
1Srijayanthi, Assistant Professor, Department of CSE, R.M.K. Engineering College Kavaraipettai, Tamil Nadu, India.
2T.Sethukarasi, Professor, Department of CSE, R.M.K. Engineering College Kavaraipettai, Tamil Nadu, India.
3A.Thilagavathy, Associate Professor, Department of CSE, R.M.K. Engineering College Kavaraipettai, Tamil Nadu, India.
Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 54-57 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4486119119/2019©BEIESP | DOI: 10.35940/ijitee.A4486.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The data of medical applications over the internet contains sensitive data. There exist several methods that provide privacy for these data. Most of the privacy-preserving data mining methods make the assumption of the separation of quasi-identifiers (QID) from multiple sensitive attributes. But in reality, the attributes in a dataset possess both the features of QIDs and sensitive data. In this paper privacy model namely (vi…vj)-diversity is proposed. The proposed anonymization algorithm works for databases containing numerous sensitive QIDs. The real dataset is used for performance evaluation. Our system reduced the information loss for even huge number of attributes and the values of sensitive QID’s are protected.
Keywords: Quasi-identifiers, Sensitive Attribute, Anonymization..
Scope of the Article: Algorithm Engineering