<![CDATA[In the current scenario of modern era, providing security to an individual is always a matter of concern when a huge volume of electronic data is gathering daily. Now providing security to the gathered data is not only a matter of concern but also remains a notable topic of research. The concept of Privacy Preserving Data Publishing (PPDP) defines accessing the published data without disclosing the non required information about an individual. Hence PPDP faces the problem of publishing useful data while keeping the privacy about sensitive information about an individual. A variety of techniques for anonymization has been found in literature, but suffers from different kind of problems in terms of data information loss, discernibility and average equivalence class size. This paper proposes amalgamated approach along with its verification with respect to information loss, value of discernibility and the value of average equivalence class size metric. The result have been found encouraging as compared to existing k-anonymity based algorithms such as Datafly, Mondrian and Incognito on various publically available datasets.]]>
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