A Secure and Privacy Preserving Telehealth Solution in Fog Based Environment

Abstract

The emergence of smart health facilitates readily available healthcare services. Increased demand for medical services, on the other hand, necessitates additional computing and storage resources near patients/users for smart sensing, analysis and processing. Fog Computing (FC) is a rapidly evolving field, which is considered as a valuable addition to the cloud to address issues such as unpredictable latency, resource constraints, confidentiality, and easy accessibility. Since information can be easily stored and assessed relatively close to sources of information on native fog nodes, it is relatively safe as compared to cloud computing. Still, the existing fog models face number of challenges, and focuses on one of two things: accuracy of data obtained or low turnaround time, not both. This paper proposes SPATS, a Secure AES encryption enabled Privacy Assured Telehealth System that addresses privacy and security threats in a fog environment by integrating stacking classifier in fog devices and deploying it in a real-world application of automatic health analysis. The AES encryption technology is used to ensure privacy and security from attackers while sensitive data is stored in cloud. A detailed experimentation and analysis have been done using EHR dataset from real-world medical services to assess the performance of SPATS. The results of the experiments reveal that the proposed system accurately predicts the health condition. When compared to existing machine learning techniques, the suggested approach achieves a better prediction accuracy.