Towards an Efficient Privacy-Preserving Decision Tree Evaluation Service in the Internet of Things

• Published in: Symmetry (Basel) Vol. 12; no. 1; p. 103
• Main Authors: Liu, Lin, Su, Jinshu, Zhao, Baokang, Wang, Qiong, Chen, Jinrong, Luo, Yuchuan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2020
• Subjects: Classification; Cloud computing; Communication; Costs; Data mining; Decision making; Decision trees; Design; Evaluation; Internet of Things; Privacy; Wireless networks
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym12010103

With the fast development of the Internet of Things (IoT) technology, normal people and organizations can produce massive data every day. Due to a lack of data mining expertise and computation resources, most of them choose to use data mining services. Unfortunately, directly sending query data to the cloud may violate their privacy. In this work, we mainly consider designing a scheme that enables the cloud to provide an efficient privacy-preserving decision tree evaluation service for resource-constrained clients in the IoT. To design such a scheme, a new secure comparison protocol based on additive secret sharing technology is proposed in a two-cloud model. Then we introduce our privacy-preserving decision tree evaluation scheme which is designed by the secret sharing technology and additively homomorphic cryptosystem. In this scheme, the cloud learns nothing of the query data and classification results, and the client has no idea of the tree. Moreover, this scheme also supports offline users. Theoretical analyses and experimental results show that our scheme is very efficient. Compared with the state-of-art work, both the communication and computational overheads of the newly designed scheme are smaller when dealing with deep but sparse trees.