Lightweight Privacy-preserving Medical Diagnosis in Edge Computing

Abstract

In the era of machine learning, mobile users are able to submit their symptoms to doctors at any time, anywhere for personal diagnosis. It is prevalent to exploit edge computing for real-time diagnosis services in order to reduce transmission latency. Although data-driven machine learning is powerful, it inevitably compromises privacy by relying on vast amounts of medical data to build a diagnostic model. Therefore, it is necessary to protect data privacy without accessing local data. However, the blossom has also been accompanied by various problems, i.e., the limitation of training data, vulnerabilities, and privacy concern. As a solution to these above challenges, in this paper, we design a lightweight privacy-preserving medical diagnosis mechanism on edge. Our method redesigns the extreme gradient boosting (XGBoost) model based on the edge-cloud model, which adopts encrypted model parameters instead of local data to reduce amounts of ciphertext computation to plaintext computation, thus realizing lightweight privacy preservation on resource-limited edges. Additionally, the proposed scheme is able to provide a secure diagnosis on edge while maintaining privacy to ensure an accurate and timely diagnosis. The proposed system with secure computation could securely construct the XGBoost model with lightweight overhead, and efficiently provide a medical diagnosis without privacy leakage. Our security analysis and experimental evaluation indicate the security, effectiveness, and efficiency of the proposed system.