IJIRST (International Journal for Innovative Research in Science & Technology)ISSN (online) : 2349-6010

 International Journal for Innovative Research in Science & Technology

Safeguarding the Privacy Preservation of Data in Data mining: A Survey


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International Journal for Innovative Research in Science & Technology
Volume 1 Issue - 7
Year of Publication : 2014
Authors : Amit Sindhi ; Rohan Prajapati

BibTeX:

@article{IJIRSTV1I7049,
     title={Safeguarding the Privacy Preservation of Data in Data mining: A Survey},
     author={Amit Sindhi and Rohan Prajapati},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={1},
     number={7},
     pages={182--184},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV1I7049.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}



Abstract:

Sharing, transferring, mining and publishing data are fundamental operations in day to day life. Preserving the privacy of individuals is essential one. Sensitive personal information must be protected when data are published. There are two kinds of risks namely attributing disclosure and identity disclosure that affects privacy of individuals whose data are published. Early Researchers have contributed new methods namely k-anonymity, l-diversity, t-closeness to preserve privacy. K-anonymity method preserves privacy of individuals against identity disclosure attack alone. But Attribute disclosure attack makes compromise this method. Limitation of k-anonymity is fulfilled through l-diversity method. But it does not satisfy the privacy against identity disclosure attack and attribute disclosure attack in some scenarios. The efficiency of t-closeness method is better than k-anonymity and l-diversity. But the complexity of Computation is more than other proposed methods. The k-anonymity method use for preserving the privacy of individuals’ sensitive information from attribute and identity disclosure attacks [1].


Keywords:

Data Privacy, Generalization, Anonymization, Suppression, Privacy Preservation, Data Publishing


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