1. An adequate amalgamated approach for anonymization
Authors : Deepak Narula, Pardeep Kumar, Shuchita Upadhyaya
Pages : 228-232
DOI : http://dx.doi.org/10.21172/1.102.36
Keywords : Privacy Preserving Data Publishing (PPDP) , Fuzzy Logic, Discernibility ,Threshold, fuzzify Abstract :Digital era empowered with data sharing for the purpose of research and increasing business prospective. However the collected data some time contains sensitive information that need not to be disclosed publically as if that data is available publically it can be a threat to the privacy. So, data privacy is the most crucial act in data publishing. Various methods for anonymization have been suggested in literature. Out of these methods k-anonymization is one of the fundamental and most popular approach but suffering from the shortcoming of homogeneity and background attack. This paper is an attempt to propose an amalgamated approach with less information loss, discernibility cost and the value of average equivalence class size. Moreover, this also increases the data usability and also supports the privacy of sensitive information. The new proposed method can be practically implemented and works even if the domain set of sensitive information is small.
Citing this Journal Article :Deepak Narula, Pardeep Kumar, Shuchita Upadhyaya, "An adequate amalgamated approach for anonymization", Volume 10 Issue 2 - April 2018, 228-232
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