Publicly verifiable and secure SVM classification for cloud-based health monitoring services

• Main Authors: LEI, Dian, LIANG, Jinwen, ZHANG, Chuan, LIU, Ximeng, HE, Daojing, ZHU, Liehuang, GUO, Song
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Cloud computing; public verification; remote health monitoring services; secure support vector machine (SVM) classification; Databases and Information Systems; Electrical and Computer Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/8757
• Institution: Singapore Management University

In cloud-based health monitoring services, healthcare centers often outsource support vector machine (SVM)-based clinical decision models to provide remote users with clinical decisions. During service provisioning, authorized external organizations like insurance companies aim to verify decision correctness to prevent fraudulent medical reimbursements. However, existing verifiable and secure SVM classification schemes have predominantly focused on user self-verification, thereby introducing potential risks of privacy leakage (such as input data exposure) in publicly verifiable scenarios. To address the aforementioned limitation, we propose a publicly verifiable and secure SVM classification scheme (PVSSVM) for cloud-based health monitoring services in a malicious setting, which can accommodate the verification needs of users or authorized external organizations with respect to potential malicious results returned by cloud servers. Specifically, we utilize homomorphic encryption and secret sharing to protect the model and data confidentiality in the cloud server, respectively. Based on a multiserver verifiable computation framework, PVSSVM achieves public verification of predicted results. Additionally, we further investigate its performance. Experimental evaluations demonstrate that PVSSVM outperforms existing state-of-the-art solutions in terms of computation and communication overhead. Notably, in the verification scenario of large-scale predictions, the proposed scheme achieves a reduction of approximately 83.71% in computation overhead through batch verification, as compared to one-by-one verification.