Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
Disclosure Limitation of Sensitive Rules
KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
Mining Generalized Association Rules Using Pruning Techniques
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
IEEE Transactions on Knowledge and Data Engineering
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
Privacy-Preserving Frequent Pattern Mining across Private Databases
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Hiding Sensitive Association Rules with Limited Side Effects
IEEE Transactions on Knowledge and Data Engineering
An Efficient Data Structure for Mining Generalized Association Rules
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Privacy Preserving Association Rules by Using Greedy Approach
CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 04
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In a very large database, there exists sensitive information that must be protected against unauthorized accesses. The confldentiality 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.