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2017 IEEE Second International Conference on Data Science in Cyberspace (DSC) (2017)
Shenzhen, Guangdong, China
June 26, 2017 to June 29, 2017
ISBN: 978-1-5386-1600-0
pp: 124-132
ABSTRACT
Many companies want to share data for datamining tasks. However, the privacy and security concerns become a bottleneck in the data sharing filed. The secure multiparty computation (SMC)-based privacy-preserving data mining has emerged as a solution to this problem. However, traditional SMC solutions are inefficient. We introduce the method of outsourcing to reduce the computation cost of user’s side. In order to preserve the privacy of the sharing data, we propose an outsourced privacy-preserving C4.5 algorithm on arbitrarily partitioned databases based on the outsourced accountable computing for finding frequent itemsets (OACFFI) and the outsourced weighted average computing (OWAC) protocols. As a result, we show that our method can achieve similar result with the original C4.5 decision tree algorithm, but also preserve the privacy of the data. We prove that there is a sublinear relationship between the computational cost of user side and the number of participating parties.
INDEX TERMS
Protocols, Servers, Public key, Decision trees, Encryption, Partitioning algorithms
CITATION

Y. Li, Z. L. Jiang, X. Wang, S. Yiu and Q. Liao, "Outsourced Privacy-Preserving C4.5 Algorithm over Arbitrarily Partitioned Databases," 2017 IEEE Second International Conference on Data Science in Cyberspace (DSC), Shenzhen, Guangdong, China, 2017, pp. 124-132.
doi:10.1109/DSC.2017.80
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