K-ANONYMITY: The History of an IDEA
Swagatika Devi, Department Of Computer Science and Engineering, ITER Siksha ‘O’ Anusandhan University, Khandagiri Square, Bhubaneswar ,Orissa, India-751030 India.
Manuscript received on February 15, 2012. | Revised Manuscript received on February 20, 2012. | Manuscript published on March 05, 2012. | PP: 347-352 | Volume-2 Issue-1, March 2012. | Retrieval Number: A0449022112/2012©BEIESP
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© The Authors. Published By: 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: Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called k-anonymity has gained popularity. In a k-anonymized dataset, each record is indistinguishable from at least k−1 other records with respect to certain “identifying” attributes. In this paper, we discuss the concept of k-anonymity, from its original proposal illustrating its enforcement via generalization and suppression. We also discuss different ways in which generalization and suppressions can be applied to satisfy k- anonymity. By shifting the concept of k-anonymity from data to patterns, we formally characterize the notion of a threat to anonymity in the context of pattern discovery. We provide an overview of the different techniques and how they relate to one another. The individual topics will be covered in sufficient detail to provide the reader with a good reference point. The idea is to provide an overview of the field for a new reader from the perspective of the data mining community.
Keywords: K-Anonymity, Generalization, Suppression, Pattern discovery.