Secure Health Fog: A Novel Framework for Personalized Recommendations Based on Adaptive Model Tuning

Abstract

The emergence of smart technology has equipped humans with wearables and sensors that collect relevant data related to individuals and their surroundings. In healthcare, the collected data can monitor user emotion, behavior, and activity, which leads to the development of personalized decisions and improves lifestyle. In this paper, we analyzed the existing health fog framework and identified its limitations in terms of security, performance, and accuracy. Based on these limitations, we propose a secure health fog (SHF) framework that collects data from different Internet of Things (IoT) devices and maintains a personalized repository for adaptive model tuning. The adaptive model improves periodically based on user feedback and generates a personalized recommendation. Moreover, the existing IoT devices mostly rely on low-cost and low-power Zigbee technology, which is vulnerable to different attacks, such as device control, eavesdropping, fake device injection, malicious insider, man-in-the-middle, masquerading, message tampering, privacy leakage, and replay attack. Therefore, we propose a Zigbee Secure Health Fog (ZigbeeSHF) protocol, which uses symmetric and public-key cryptography to prevent these attacks. For data migration security, we concatenate the encrypted data with the encrypted digital signature to provide data authenticity, integrity, and confidentiality. To support our claims, we use the automated formal verification tools Scyther and AVISPA (Automated Validation of Internet Security Protocols and Applications), which evaluate the protocols based on the threat model and exploits the vulnerabilities in different attacking environments. The results of both tools ensure prevention against the mentioned attacks. As a proof of concept, we also evaluate the accuracy and performance of our proposed framework in a smart studio apartment. The result shows that the adaptive model tuned for an individual user is very effective, and the average accuracy of 32.5% is improved after one month. Furthermore, the proposed ZigbeeSHF protocol requires 7.93% and 25.35% more computation time than Zigbee with and without installation code, respectively.

Keywords

• Secure health fog
• adaptive model tuning
• personalized decision
• security
• Zigbee