A Novel Method For Outsource Medical Data Securely

Abstract

Medical imaging is essential for diagnosing illnesses, and due to the
delicate nature of medical images, stringent security and privacy measures must be
in place. Medical images should be secured before being outsourced in a cloudbased medical system for Healthcare Industry 4.0. However, it is difficult and
currently impractical to process queries over encrypted data without first performing
the decryption step. In the paper, we suggest an effective method for locating the
precise nearest neighbour in a set of encrypted medical photos. By obtaining the
lower bound of the Euclidean distance, which is correlated with the mean and
standard deviation of the data, we can eliminate candidates instead of computing the
Euclidean distance. Our method can find the precise nearest neighbour as opposed to
an approximation, unlike the majority of other existing approaches. We then assess
our suggested strategy to show its usefulness.