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Blockchain-Based Decision Tree Classification in Distributed Networks

Jianping Yu1,2,3, Zhuqing Qiao1, Wensheng Tang1,2,3,*, Danni Wang1, Xiaojun Cao4

1 College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, P.R. China
2 Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, 410081, P.R. China
3 Hunan Xiangjiang Artificial Intelligence Academy, Changsha, 410000, P.R. China
4 Department of Computer Science, Georgia State University, Atlanta, 30303, USA

* Corresponding Author: Wensheng Tang. Email: email

Intelligent Automation & Soft Computing 2021, 29(3), 713-728.


In a distributed system such as Internet of things, the data volume from each node may be limited. Such limited data volume may constrain the performance of the machine learning classification model. How to effectively improve the performance of the classification in a distributed system has been a challenging problem in the field of data mining. Sharing data in the distributed network can enlarge the training data volume and improve the machine learning classification model’s accuracy. In this work, we take data sharing and the quality of shared data into consideration and propose an efficient Blockchain-based ID3 Decision Tree Classification (BIDTC) framework for distributed networks. The proposed BIDTC takes advantage of three techniques: blockchain-based ID3 decision tree, enhanced homomorphic encryption, and stimulation smart contract to conduct classification while effectively considering the data privacy and the value of user data. BIDTC employs the data federation scheme based on homomorphic encryption and blockchain to achieve more training data sharing without sacrificing data privacy. Meanwhile, smart contracts are integrated into BIDTC to incentivize users to share more high-quality data. Our extensive experiments have demonstrated that the proposed BIDTC significantly outperforms existing schemes in constructed consortium blockchain networks.


Cite This Article

J. Yu, Z. Qiao, W. Tang, D. Wang and X. Cao, "Blockchain-based decision tree classification in distributed networks," Intelligent Automation & Soft Computing, vol. 29, no.3, pp. 713–728, 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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