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Wireless Communications and Mobile Computing
Volume 2018, Article ID 2385150, 10 pages
Research Article

Securely Outsourcing ID3 Decision Tree in Cloud Computing

1Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China
2Jinan University, Guangzhou, China
3Henan Normal University, Henan, China
4School of Computer Science, Guangzhou University, China

Correspondence should be addressed to Zoe L. Jiang; nc.ude.tih@gnaijleoz

Received 2 May 2018; Accepted 2 September 2018; Published 4 October 2018

Academic Editor: Jaime Lloret

Copyright © 2018 Ye Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


With the wide application of Internet of Things (IoT), a huge number of data are collected from IoT networks and are required to be processed, such as data mining. Although it is popular to outsource storage and computation to cloud, it may invade privacy of participants’ information. Cryptography-based privacy-preserving data mining has been proposed to protect the privacy of participating parties’ data for this process. However, it is still an open problem to handle with multiparticipant’s ciphertext computation and analysis. And these algorithms rely on the semihonest security model which requires all parties to follow the protocol rules. In this paper, we address the challenge of outsourcing ID3 decision tree algorithm in the malicious model. Particularly, to securely store and compute private data, the two-participant symmetric homomorphic encryption supporting addition and multiplication is proposed. To keep from malicious behaviors of cloud computing server, the secure garbled circuits are adopted to propose the privacy-preserving weight average protocol. Security and performance are analyzed.