Abstract: Aiming at the shortcomings of the global and local algorithms of the privacy preserving in data publishing, a method of top-down personalized generalization backtracking algorithm and its expansion algorithm based on the K-anonymous were proposed. The method combines the L-diversity and (s, d)-anonymization and dynamically build generalization tree structure, by which users can customize the privacy security levels and separate the similar level of safety as far as possible, so as to guarantee the availability and security of the information, which can effectively prevent homogeneity attack and background knowledge attack. Based on the above thinking. A system of personalized privacy preservation based on K-anonymization was developed on J2SE platform. The comprehensive experimental data shows that the algorithm can improves security and guarantee the availability of information effectively.

Key words: privacy preservation, K-anonymization, generalization, information security, personalized