A Privacy Preserving Computer-aided Medical Diagnosis Framework with Outsourced Model

计算机科学 软件部署 架空(工程) 加密 方案(数学) 访问控制 计算机安全 机器学习 人工智能 软件工程 数学 操作系统 数学分析
作者
Xiaobing Feng,Qingni Shen,Cong Liu,Xuewei Wang,Niantao Xie,Luyuan Xie,Yuejian Fang,Zhonghai Wu
标识
DOI:10.1109/bibm58861.2023.10385713
摘要

Computer-aided diagnosis plays an increasingly important role in modern medical activities, relying largely on the deployment of medical machine learning models. Protecting the security of model parameters is crucial for model providers. However, the current schemes for protecting model parameters are mostly interactive. This interactive nature makes it difficult to support offline deployment of models and flexible authorization of prediction results, thus hindering the widespread application of computer-aided diagnosis. To address these limitations, we propose a new computer-aided medical diagnosis framework by designing a new identity-based inner product functional proxy re-encryption (IB-IPFPRE) scheme. Our framework supports private deployment of medical diagnostic models without compromising model parameters. It also enables access control of prediction results based on user identity. Compared to existing privacy-preserving prediction techniques, our framework significantly reduces communication overhead and does not require the model owner to be online in real-time. Furthermore, our scheme enables flexible delegation of prediction results, allowing users to authorize the sharing of prediction results with other entities as needed. We conducted extensive experiments for logistic regression on three medical datasets. The experiments demonstrate that our scheme achieved 40% to 7× performance improvement in LAN environment and 13× to 15× improvement in WAN environment, and did not require any communication overhead during the privacy preserving prediction phase.

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