A Hybrid Approach to Privacy Preserving in Association Rules Mining

Narges Jamshidian Ghalehsefidi, Mohammad Naderi Dehkordi


Nowadays, data mining is a useful, yet dangerous technology through which useful information and the relationships between items in a database are detected. Today, companies and users need to share information with others for their progress and they should somehow manage this information sharing for preserving sensitive information. Privacy preserving in data mining was introduced for managing information sharing. This paper presents a hybrid algorithm with distortion technique with both support-based and confidence-based approaches for privacy preserving. The proposed algorithm tries to maintain useful association rules and hide sensitive rules from the perspective of the database owner. It also has no limit on the number of items in the left-hand side and the right-hand side of rules. This paper also compares the proposed algorithm with MDSRRC algorithm and 1.b algorithm. The proposed algorithm has less lost rules compared with the MDSRRC and 1.b algorithms and its CPU usage is less then.


privacy preserving; hiding sensitive rules; helpful association rules

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