Secure SVM Training Over Vertically-Partitioned Datasets Using Consortium Blockchain for Vehicular Social Networks

By Meng Shen, Jie Zhang, Liehuang Zhu, Ke Xu, Xiangyun Tang. 2020

71 citations recorded by the source corpus.

Machine learning (ML) techniques are expected to be used for specific applications in Vehicular Social Networks (VSNs). Support vector machine (SVM) is one of the typical ML methods and widely used for its high efficiency. Due to the limitation of data sources, the data collected by different entities usually contain attributes that are quite different. However, in some real-world scenarios, when training an SVM classifier, many entities face the same problem that they are lacking in data with adequate attributes. Thus multiple entities are required to share data to combine a dataset with diverse attributes and then jointly train a comprehensive classifier. However, data privacy concerns are raised because of data sharing. To sovle the problem, we propose a privacy-preserving SVM classifier training scheme over vertically-partitioned datasets posessed by multiple data providers. In our scheme, we utilize consortium blockchain and threshold homomorphic cryptosystem to establish a secure SVM classifier training platform without a trusted third-party. We keep lots of training operations locally over original data and necessary interactions between participants are protected by the threshold Paillier and consortium blockchain. Security analysis proves that our scheme can preserve the privacy of the original data and the training intermediate values. Extensive experiments indicate that our scheme has high efficiency and no accuracy loss.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiaojiang Du
2. Ke Xu
3. Mohsen Guizani
4. Meng Shen
5. Liehuang Zhu
6. Hongbo Yu

In Computer Vision and Pattern Recognition

1. Xiaojiang Du
2. Mohsen Guizani
3. Hongbo Yu

In Information Systems

1. Xiaojiang Du
2. Mohsen Guizani
3. Liehuang Zhu

Citations in the corpus

• SplitNN-driven Vertical Partitioning (2020, neutral): “Vertically partitioned SVM The work in (Shen et al. , 2019) shows a threshold Paillier and blockchain based secure approach for using support vector machines on vertically partitioned data.”
• A Survey on Blockchain for Big Data: Approaches, Opportunities, and Future Directions (2020, unknown): “[68] Data training/learning process.”
• Blockchain-Assisted Secure Device Authentication for Cross-Domain Industrial IoT (2020, methodology): “Blockchain has been used as the supporting technology in multi-party solutions [12], [13].”