A Secure and Privacy-Preserving Technique Based on Contrast-Enhancement Reversible Data Hiding and Plaintext Encryption for Medical Images
Yang Yang, Xingxing Xiao, Xue Cai, Weiming Zhang
Abstract
Protection of medical data has become a prerequisite in medical imaging clouds due to the semi-trusted cloud. Aiming at preserving patients' privacy and increasing the security of medical images in the cloud, this letter proposes a secure and privacy-preserving technique which provides a new security mechanism for medical data. In this technique, a novel reversible data hiding (RDH) based on adaptive texture classification is proposed to embed privacy data into medical images for preserving patients' privacy and improving image quality, and plaintext encryption is proposed to encrypt the marked medical image into the similar image of target image for increasing image security. Extensive experiments have shown that the proposed RDH is better than other RDH methods. Plaintext encryption can reduce the attacker's attention and increase the security of medical images well.