Since medical imaging is essential for diagnosing illnesses, stringent security and privacy laws must be put in place due to the sensitive nature of these pictures. Prior to being outsourced, medical images in cloud-based medical systems should be protected for Healthcare Industry 4.0. However, it is presently difficult and impossible to perform queries over encrypted data without first finishing the decryption step. In the paper, we provide a useful method for locating the precise neighbor inside a set of encrypted medical photos. We may exclude candidates by finding the lower limit of the distance, which is related to the mean and standard deviation of the data, as opposed to calculating the Euclidean distance. Our technique finds the actual closest neighbor instead of an approximation, unlike most other current methods. We then assess our suggested method to prove its effectiveness.

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