Research

Towards Practical Privacy-Preserving Processing over Encrypted Data in IoT: An Assistive Healthcare Use Case

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Abstract

With the advancement of Internet of Things (IoT), a large number of electronic devices are connected to the Internet. These connected electronic devices acquire, transmit information and respond to any received actions. In medical ecosystem, hospitals can implement medical diagnosis with medical sensors, especially for remote auxiliary medical diagnosis. But, in this context, patients privacy is paramount importance, and confidentiality of medical data is crucial. Therefore, the main challenge ahead is how to realize remote auxiliary medical diagnosis while protecting confidentiality of the medical data and ensuring patients privacy. In this paper, based on somewhat homomorphic encryption (SHE) scheme addressed by Junfeng Fan and Frederik Vercauteren (FV), we provide the first instance of a new efficient SHE scheme for homomorphic evaluation over Single Instruction Multiple Data (SIMD). We also implement a new set of efficient SIMD homomorphic comparison and division schemes. Based on these findings, we implement efficient privacy-preserving and SIMD homomorphic surf and multi-retina-image matching schemes. Offered functionalities include SIMD homomorphic feature point detection, multi-retina-image matching and lesion detection for the encrypted retinal image of diabetic retinopathy. Finally, we provide a proof-of-concept application implementation towards remote auxiliary diagnosis systems for diabetes in order to showcase the core security and privacy pillars of our solution. In the meantime, our IoT system designed with lattice-based cryptography preserves data confidentiality under quantum computation and quantum computers.