Internet of Medical Things (IoMTs) 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 IoMTs 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 modellers, 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 modelling; results from the simulation will be provided to authorities for policymaking and pandemic control. However, concerns are raised on data breaches from modellers. In reality, sharing the data in clear with modellers 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 IoMTs. 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 modelling. By comparisons with a differential privacy-based solution, we demonstrate the favorable feasibility, accuracy and security of the proposed solution.

AI-as-a-Service has emerged as an important trend for supporting the growth of the digital economy. Digital service providers make use of their vast amount of customer data to train AI models (such as image recognition, financial modelling and pandemic modelling etc) and offer them as a service on the cloud. While there are convincing advantages for using such third-party models, the fact that model users are required to upload their data to the cloud is bound to raise serious privacy concerns, especially in the face of increasingly stringent privacy regulations and legislation. To promote the adoption of AI-as-a-Service while addressing privacy issues, we propose a practical approach for constructing privacy-enhanced neural networks by designing an efficient implementation of fully homomorphic encryption. With this approach, an existing neural network can be converted to process FHE-encrypted data and produce encrypted output which are only accessible by the model users, and more importantly, within an operationally acceptable time (e.g. within 1 second for facial recognition in typical border control systems). Experimental results show that in many practical tasks such as facial recognition, text classification and so on, we obtained the state-of-the-art inference accuracy in less than one second on a 16 cores CPU.

Searchable compressed data structures (e.g.Burrows-Wheeler Transform) enable one to create a memory-efficient index for large datasets such as human genomes. On the other hand, storing such an index in a third-party server, e.g., cloud, may have the privacy and confidentiality issues. An open problem in the community is to construct a secure variant of such a data structure. This problem is challenging as most of the existing works were shown to be insecure and none of them is able to perform pattern matching. In this paper, we provide the first solution based on Burrows-Wheeler Transform (BWT) to solve this problem (our scheme can do both compression and pattern matching). A new security definition, called isomophism-restricted IND-CPA security, is proposed. We show that our scheme is secure under this definition and our scheme is practical by experiments.