Securely Outsourcing ID3 Decision Tree in Cloud Computing

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.