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Proceedings Paper

Performance evaluation of various K- anonymity techniques
Author(s): Nidhi Maheshwarkar; Kshitij Pathak; Vivekanand Chourey
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Paper Abstract

Today's advanced scenario where each information is available in one click, data security is the main aspect. Individual information which sometimes needs to be hiding is easily available using some tricks. Medical information, income details are needed to be kept away from adversaries and so, are stored in private tables. Some publicly released information contains zip code, sex, birth date. When this released information is linked with the private table, adversary can detect the whole confidential information of individuals or respondents, i.e. name, medical status. So to protect respondents identity, a new concept k-anonymity is used which means each released record has at least (k-1) other records in the release whose values are distinct over those fields that appear in the external data. K-anonymity can be achieved easily in case of single sensitive attributes i.e. name, salary, medical status, but it is quiet difficult when multiple sensitive attributes are present. Generalization and Suppression are used to achieve k-anonymity. This paper provides a formal introduction of k-anonymity and some techniques used with it l-diversity, t-closeness. This paper covers k-anonymity model and the comparative study of these concepts along with a new proposed concept for multiple sensitive attributes.

Paper Details

Date Published: 13 January 2012
PDF: 8 pages
Proc. SPIE 8350, Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies, 83501Y (13 January 2012); doi: 10.1117/12.921002
Show Author Affiliations
Nidhi Maheshwarkar, Mahakal Institute of Technology (India)
Kshitij Pathak, Mahakal Institute of Technology (India)
Vivekanand Chourey, Mandsaur Institute of Technology (India)


Published in SPIE Proceedings Vol. 8350:
Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies

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