Anonymization by Data Relocation Using Sub-clustering for Privacy Preserving Data Mining

As there are new techniques growing to reveal the hidden information on data, the threat towards those data also increases. Therefore, privacy preservation in data mining is an emerging research area which develops various algorithms to anonymize the data provided for data mining. The existing methodology handles the tradeoff between utility and privacy of data in a more expensive way in terms of execution time. In this paper, a simple Anonymization technique using subclustering is specified which achieves maximum privacy and also utility with minimum execution time. The methodology is explained with algorithm and the results are compared with the baseline method.