Disease Prediction in Edge Computing: A Privacy-Preserving Technique for PHI Collection and Analysis

Edge computing has garnered significant attention in recent years, as it enables the extension of cloud resources to the network edge. This enables the user to utilize virtually enhanced resources in terms of storage and computation at a lower cost. The edge-computing-assisted wireless wearable communication (EWWC) technology is a prime candidate for e-health edge applications to collect personal health information, which leads to disease learning and prediction. Ensuring privacy and efficiency of such a system in EWWC is extremely important. In this article, we introduce an efficient and privacy-preserving disease prediction scheme. We use the randomizable signature and matrices encryption technique to achieve identity protection and data privacy. The experimental analysis shows that our solution outperforms the existing solution in terms of computational costs and communication overhead. At the same time, it is able to provide data privacy, prediction model security, user identity protection, mendacious data traceability, and model verifiability. We also analyze potential future research directions related to this emerging area.

Automated summary

This article introduces an efficient and privacy-preserving disease prediction scheme using randomizable signature and matrices encryption technique. It outperforms the existing solution in terms of data privacy and user identity protection.