An Efficient FHE-Enabled Secure Cloud-Edge Computing Architecture for IoMT Data Protection With its Application to Pandemic Modeling

Internet of Medical Things (IoMT) is revolutionizing the healthcare industry regarding how diagnosis process takes place, how treatment is provided, and how public health policies are made. A real-world use case of IoMT is to investigate how infectious diseases, e.g., COVID-19, spread in a population through social events. In this use case, people's social contact records in certain venues are collected by sensors and saved locally; pandemic modelers, as third-party vendors, are desired to construct social contact network based on contacts records, and to simulate the process of disease transmission over the contact network by transmission modeling; results from the simulation will be provided to authorities for policymaking and pandemic control. However, concerns are raised on data breaches from modelers. In reality, sharing the data in clear with modelers is not allowed by regulations for the sake of privacy. In this work, we will be addressing the contradiction between data privacy and usability when vendors are involved in IoMT. We propose a secure cloud-edge computing architecture based on an efficient fully homomorphic encryption (FHE) scheme. This architecture allows vendors to securely and "blindly" process medical data without compromising the quality of their service. Moreover, we apply the proposed architecture to the use case of pandemic modeling. By comparisons with a differential privacy-based solution, we demonstrate the favorable feasibility, accuracy, and security of the proposed solution.