A Fast Nearest Neighbor Search Scheme over Outsourced Encrypted Medical Images

Description

Medical imaging is crucial for medical diagnosis, and the sensitive nature of medical images necessitates rigorous security and privacy solutions to be in place. In a cloud-based medical system for Healthcare Industry 4.0, medical images should be encrypted prior to being outsourced. However, processing queries over encrypted data without first executing the decryption operation is challenging and impractical at present. In this project, we propose a secure and efficient scheme to find the exact nearest neighbor over encrypted medical images. Instead of calculating the Euclidean distance, we reject candidates by computing the lower bound of the Euclidean distance that is related to the mean and standard deviation of data. Unlike most existing schemes, our scheme can obtain the exact nearest neighbor rather than an approximate result. We, then, evaluate our proposed approach to demonstrate its utility.