Anonymization in PPDM based on Data Distributions and Attribute Relations

Jitendra Kumar Jaiswal, Rita Samikannu, Ilango Paramasivam
2016 Indian Journal of Science and Technology  
Objectives: Privacy Preserving Data Mining techniques deal with the secure data publication or communication without revealing the private and sensitive information about any individual. Anonymization technique has been considered as one of the most effective techniques since it can provide better tradeoff between data utility and privacy preservation. Methods/Statistical Analysis: Existing anonymization techniques works on individual attributes and their cardinalities and they do not consider
more » ... he relations among different attributes of the data. In this paper we have considered auxiliary information and entropy and mutual information to calculate distribution of entities in an attribute and relations among different attributes respectively. Based on these calculations we shall be analyzing the best generalization level for data anonymization.
doi:10.17485/ijst/2016/v9i37/94290 fatcat:me64xnyhoncz7jmcbyusungkqi