Lightweight Privacy-Preserving Medical Diagnosis in Edge Computing

Abstract

With the development of machine learning, it is popular that mobile users can submit individual symptoms at any time anywhere for medical diagnosis. Edge computing is frequently adopted to reduce transmission latency for real-time diagnosis service. However, the data-driven machine learning, which requires to build a diagnosis model over vast amounts of medical data, inevitably leaks the privacy of medical data. It is necessary to provide privacy preservation. To solve above challenging issues, in this article, we design a lightweight privacy-preserving medical diagnosis mechanism on edge, called LPME. Our LPME redesigns the extreme gradient boosting (XGBoost) model based on the edge-cloud model, which adopts encrypted model parameters instead of local data to remove amounts of ciphertext computation to plaintext computation, thus realizing lightweight privacy preservation on resource-limited edge. In addition, LPME provides secure diagnosis on edge with privacy preservation for private and timely diagnosis. Our security analysis and experimental evaluation indicates the security, effectiveness, and efficiency of LPME.