Clustering based Anonymization for privacy preservation

While registering on social networking site, it is necessary to give the personal information; some of this information is sensitive and needed to be preserved. To sustain the privacy of user on a social network Anonymization technique is employed. In Anonymization approach individuals personal information is either mask or remove from the dataset so individual´s data become anonymous. When a dataset is released it is important to prevent data from unwanted disclosure, balance the usefulness and privacy of published dataset. Proposed work gives the Anonymized view of a data set and the result of implementation of the single pass k-means Anonymization algorithm. To Anonymized the dataset generalization and suppression approaches are used.