Privacy-Preserving Support Vector Machine Training Over Blockchain-Based Encrypted IoT Data in Smart Cities

• Published in: IEEE internet of things journal 2019-10, Vol.6 (5), p.7702-7712
• Main Authors: Shen, Meng, Tang, Xiangyun, Zhu, Liehuang, Du, Xiaojiang, Guizani, Mohsen
• Format: Article
• Language: English
• Subjects: Algorithms; Anomalies; Blockchain; Computational modeling; Cryptography; Data models; Data privacy; Data retrieval; Distributed ledger; encrypted Internet of Things (IoT) data; Encryption; homomorphic cryptosystem (HC); Internet of Things; Iterative methods; Machine learning; machine learning (ML); Multiplication; Polynomials; Privacy; privacy protection; Smart cities; Support vector machines; Training; Trusted third parties
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2019.2901840

Machine learning (ML) techniques have been widely used in many smart city sectors, where a huge amount of data is gathered from various (IoT) devices. As a typical ML model, support vector machine (SVM) enables efficient data classification and thereby finds its applications in real-world scenarios, such as disease diagnosis and anomaly detection. Training an SVM classifier usually requires a collection of labeled IoT data from multiple entities, raising great concerns about data privacy. Most of the existing solutions rely on an implicit assumption that the training data can be reliably collected from multiple data providers, which is often not the case in reality. To bridge the gap between ideal assumptions and realistic constraints, in this paper, we propose secureSVM, which is a privacy-preserving SVM training scheme over blockchain-based encrypted IoT data. We utilize the blockchain techniques to build a secure and reliable data sharing platform among multiple data providers, where IoT data is encrypted and then recorded on a distributed ledger. We design secure building blocks, such as secure polynomial multiplication and secure comparison, by employing a homomorphic cryptosystem, Paillier, and construct a secure SVM training algorithm, which requires only two interactions in a single iteration, with no need for a trusted third-party. Rigorous security analysis prove that the proposed scheme ensures the confidentiality of the sensitive data for each data provider as well as the SVM model parameters for data analysts. Extensive experiments demonstrates the efficiency of the proposed scheme.