R.Mahesh and .T.Meyyappan, Alagappa University, India

Protection of individuals’ privacy is a vital activity in data publishing. Government and public sector websites publish enormous amount of data for sharing the data among their departments and also to public for research. Sensitive information of individuals, whose data are published must be protected. Privacy is challenged through two kinds of attack namely attribute disclosure and identity disclosure. Early Research contributions were made in this direction and new methods namely k-anonymity, ℓ-diversity, t-closeness are evolved. K-anonymity method preserves the privacy against identity disclosure attack alone. It fails to address attribute disclosure attack. ℓ-diversity method overcomes the drawback of k-anonymity method. But it fails to address identity disclosure attack and attribute disclosure attack in some exceptional cases. t-closeness method is good at attribute disclosure attack. but not identity disclosure attack. Also, t-closeness method is more complex than other methods. In this paper, the authors propose a new method to preserve the privacy of individuals’ sensitive data from attribute and identity disclosure attacks. In the proposed method, privacy preservation is achieved through generalization of quasi identifier by setting range values.The proposed method is implemented and tested with various data sets. The proposed method is found to preserve the privacy of published data against attribute and identity disclosure attacks.

Data Privacy, generalization, anonymization, suppression, privacy preservation, data publishing.