Comparative Analysis of Anonymization Techniques

In recent years, the storage and management of personal data for individuals have increased rapidly, and the method of protecting personal information has grown rapidly. There are multiple purposes in which personal data can be misused. Privacy has become a major issue for high-level databases. To address these issues, several anonymization techniques have recently been suggested to safeguard data privacy. In this particular paper, we present a provisional analysis of K-anonymity, L-diversity, and T-Clogens anonymization methods based on privacy and security criteria for the higher dimensional database.