A cost-efficient and versatile sanitizing algorithm by using a greedy approach
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
Preknowledge-based generalized association rules mining
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Generalized association rule mining using an efficient data structure
Expert Systems with Applications: An International Journal
Optimized two party privacy preserving association rule mining using fully homomorphic encryption
ICA3PP'11 Proceedings of the 11th international conference on Algorithms and architectures for parallel processing - Volume Part I
Using TF-IDF to hide sensitive itemsets
Applied Intelligence
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Data mining techniques have been developed in many applications. However, they also cause a threat to privacy. In this paper, we proposed a greedy method for hiding the number of sensitive rules. The experimental results showed that the undesired side effects can be avoided in the rule hiding process by use of our approach. 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 is achieved by using an efficient data structure FCET to store solely maximal frequent itemsets rather than the entire frequent itemsets. Furthermore, we proposed a new framework for enforcing the privacy in mining association rules, that combine the techniques for efficiently hiding sensitive rules and the transaction retrieval engine based on the FCET index tree. In particular, four strategies are implemented in the sanitized procedure, for hiding a group of association rules characterized as sensitive or artificial rules.