Abstract:

A great challenge in privacy preservation is to trade off two important issues: data utility and privacy preservation, in publication of dataset which usually contain sensitive information. Anonymization is a well-represent approach to achieve this, and there exist several anonymity models. Most of those models mainly focus on protecting privacy exerting identical protection for the whole table with pre-defined parameters. This could not meet the diverse requirements of protection degrees varied with different sensitive value. Motivated by this, this paper firstly proposes an a-diversity k-anonymity model to satisfy diversity deassociation for sensitive information, and meanwhile, designs a framework based on an improved microaggregation algorithm, as an alternative to generalization/suppression to achieve anonymization. By using this framework, we improve the data utility and decrease the disclosure risk of privacy disclosure. We conduct several experiments to validate our schemes.

Key words: (a,k)-anonymity, diverse protection, privacy disclosure, microaggregation