A blockchainized privacy-preserving support vector machine classification on mobile crowd sensed data

Abstract

The voluminous amount of data generated by individuals’ mobile sensors and wearable devices is considered of a great value for the benefits of patients and clinical research. Recent advances incorporating data mining and cloud computing have leveraged the great potential of these data. However, the introduction of such technologies in the process of mobile crowd sensed data mining and analytics could potentially lead to security and privacy concerns. Individuals and organizations are not able to share and collectively run computations on their private data captured by different sensors to infer any processes of common interest. Although solutions such as Secure Multiparty Computation (SMC) were laid decades ago, they are still perceived for theoretical interest only, so far. In this paper, we aim at bridging the gap between privacy-preserving data mining and its practice. To do so, we introduce a blockchain-based privacy-preserving SVM classification (BPPSVC) between mutually distrustful data owners. In BPPSVC, blockchain technology along with smart contracts underlay more realistic assumptions about the adversarial model. Our main focus is on investigating the immutability, security and the bookkeeping properties of the blockchain in preserving the privacy of an SVM classifier over horizontally distributed IoT data. To this end, we first propose the system architecture, adversary model and design goals of BPPSVC, then we describe the design details. Our security analysis indicates that the proposed system is secure and it provides fairness and protection against Denial of Service (DoS) attacks. We finally show the efficiency and feasibility of BPPSVC through rigorous experimental results.