KAMP: Preserving k-Anonymity for Combinations of Patterns

As huge data are increasingly generated and accumulated, outsourcing data for storage, management, and knowledge discovery is becoming a paradigm. However, given that a lot of sensitive information and valuable knowledge are hidden in data, the outsourcing of data is vulnerable to privacy crises and leads to demands for generalization or suppressing techniques to protect data from re-identification attacks. Differing from previous works that aim at satisfying the k-anonymity on individual patterns, we propose the k-anonymity of multi-pattern (KAMP) problem to protect data from re-identifying users by using a combination of patterns and also propose the KAMPp1 algorithm to generalize and suppress data. To study the effectiveness of the proposed algorithm, we conduct experiments on a synthetic and a small real dataset. The experimental results show that KAMP-p1 algorithm can satisfy k-anonymity while preserving many patterns in order to retain useful knowledge for decision making.