No abstract

No abstract

With the worldwide large-scale outbreak of COVID-19, the Internet of Medical Things (IoMT), as a new type of Internet of Things (IoT)-based intelligent medical system, is being used for COVID-19 prevention and detection. However, since the widespread use of IoMT will generate a large amount of sensitive information related to patients, it is becoming more and more important yet challenging to ensure data security and privacy of COVID-19 applications in IoMT. The leakage of private information during IoMT data fusion process will cause serious problems and affect people’s willingness to contribute data in IoMT. To address these challenges, this article proposes a new privacy-enhanced data fusion strategy (PDFS). The proposed PDFS consists of four important components, i.e., sensitive task classification, task completion assessment, incentive mechanism-based task contract design, and homomorphic encryption-based data fusion. The extensive simulation experiments demonstrate that PDFS can achieve high task classification accuracy, task completion rate, task data reliability and task participation rate, and low average error rate, while improving the privacy protection for data fusion under COVID-19 application environments based on IoMT.

No abstract

Wireless mesh networks (WMNs) have emerged as a key technology for next generation wireless networks and provide a low-cost and convenient solution to the last-mile problem. Security and privacy issues are of paramount importance to WMNs for their wide deployment and for supporting service-oriented applications. Moreover, to support real-time services, WMNs must also be equipped with secure, reliable, and efficient routing protocols. Therefore, a number of research studies have been devoted to privacy-preserving routing protocols in WMNs. However, these studies cannot defend against inside attacks effectively, often take it for granted that every internal node is cooperative and trustworthy, and rarely consider dividing the user privacy information into different categories according to the security requirements. To address these issues, we propose a Privacy-Aware Secure Hybrid Wireless Mesh Protocol (PA-SHWMP), which combines a new dynamic reputation mechanism based on subject logic and uncertainty with the multi-level security technology. PA-SHWMP can defend against the internal attacks caused by compromised nodes and achieve stronger security and privacy protection while maintaining reasonable balances between security and performance. We analyze the PA-SHWMP protocol in terms of security, privacy, and performance. The simulation results show that the packet delivery ratio of the proposed PA-SHWMP becomes better than that of the existing HWMP and SHWMP protocols, when the number of malicious nodes and the percentage of lossy links increase. Moreover, the convergence time of PA-SHWMP is smaller than HWMP and SHWMP with any percentage of malicious mesh routers.