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Privacy Preserving Sequential Pattern Mining Based on Secure Two-Party Computation

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2 Author(s)
Wei-Min Ouyang ; Manage. Dept., Shanghai Univ. of Sport ; Qin-Hua Huang

Privacy-preserving data mining in distributed or grid environment is an important hot research topic in recent years. We focus on the privacy-preserving sequential pattern mining in the following situation: two parties, each having a private data set, wish to collaboratively discover sequential patterns on the union of the two private data sets without disclosing their private data to each other. Therefore, we put forward a novel approach to discover privacy-preserving sequential patterns based on secure two-party computation using homomorphic encryption technology

Published in:

Machine Learning and Cybernetics, 2006 International Conference on

Date of Conference:

13-16 Aug. 2006

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