Anonymity: An Assessment and Perspective in Privacy Preserving Data Mining

Privacy Preserving Data mining techniques depends on privacy, which captures what information is sensitive in the original data and should therefore be protected from either direct or indirect disclosure. Secrecy and anonymity are useful ways of thinking about privacy. This privacy should be measureable and entity to be considered private should be valuable. In this paper, we discuss the various anonymization techniques that can be used for privatizing data. The goal of anonymization is to secure access to confidential information while at the same time releasing aggregate information to the public. The challenge in each of the techniques is to protect data so that they can be published without revealing confidential information that can be linked to specific individuals. Also protection is to be achieved with minimum loss of the accuracy sought by database users. Different approaches of anonymization have been discussed and a comparison of the same has been provided.