International Journal of Electronic and Electrical Engineering. ISSN 0974-2174 Volume 7, Number 8 (2014), pp. 773-778 © International Research Publication House http://www.irphouse.com Comparative Analysis of Anonymization Techniques Dilpreet Kaur Arora 1 , Divya Bansal 2 and Sanjeev Sofat 3 1, 2, 3 Computer Science Department, PEC University of Technology, Chandigarh, India Abstract In recent years, privacy-preserving techniques has seen quick advancement due to rapid increase in storing and maintaining personal data about individuals. The personal data can be misused, for a variety of purposes. Maintaining the privacy for high dimensional database has become major aspect. In order to improve these concerns, a number of Anonymization techniques have recently been proposed in order to perform privacy- preservation of data. In this paper, a comparative analysis for K-Anonymity, L-Diversity and T-Closeness Anonymization techniques is presented for the high dimensional databases based upon the privacy metric. Keywords Anonymization, K-anonymity, L-diversity, t-closeness, Attributes. Introduction Due to the rapid growth in information technologies, companies at the present time collect and store huge amounts of information in their databases. Typically, such information is stored in the form of tables and each record is corresponding to an individual. Every record has a number of attributes which can be divided into three categories: 1. Explicit identifiers which can clearly identify individuals. 2. Quasi Identifying attributes whose values when taken can easily identify individuals identities. 3. Sensitive Attributes which are considered sensitive and need not be disclosed[4]. A number of different Anonymization techniques have been researched to protect the identity of the respondents. Different data holders like often remove or encrypt the explicit identifiers. While de-identifying the information which does not provide anonymity, as released information also contains other data called Quasi Identifiers which can be used for re-identifying the data respondents, thus leaking that information which is not intended to be disclosed. While releasing the information, it is necessary to protect the sensitive information of the individuals from being disclosed. While the released table gives useful information to the researchers, it also