A Cost-Efficient and Versatile Sanitizing Algorithm by Using a Greedy Approach

In a very large database, there exists sensitive information that must be protected against unauthorized accesses. The confidentiality protection of the information has been a long-term goal pursued by the database security research community and the government statistical agencies. In this paper, we proposed greedy methods for hiding sensitive rules. The experimental results showed the effectiveness of our approaches in terms of undesired side effects avoided in the rule hiding process. The results also revealed that in most cases, all the sensitive rules are hidden without generating spurious rules. First, the good scalability of our approach in terms of database sizes was achieved by using an efficient data structure FCET to store only maximal frequent itemsets instead of storing all frequent itemsets. Furthermore, we also proposed a new framework for enforcing the privacy in mining association rules. In the framework, we combined the techniques for efficiently hiding sensitive rules with the transaction retrieval engine based on the FCET index tree. For hiding sensitive rules, the proposed greedy approach includes a greedy approximation algorithm and a greedy exhausted one to sanitize the database. In particular, we presented four strategies in the sanitizing procedure and four strategies in the exposed procedure, respectively, for hiding a group of association rules characterized as sensitive or artificial rules. In addition, the exposed procedure would expose missing rules during the processing so that the number of missing rules could be lowered as possible as we can.