Secure Retrieval of Brain Tumor Images Using Perceptual Encryption in Cloud-Assisted Scenario

Scarcity of data is one of the major challenges in developing automatic computer-aided diagnosis systems, training radiologists and supporting medical research. One solution toward this is community cloud storage, which can be utilized by organizations with a common interest as a shared data repository for joint projects and collaboration. In this large database, relevant images are often searched by an image retrieval system, for which the computation and storage capabilities of a cloud server can bring the benefits of high scalability and availability. However, the main limitation in availing third party-provided services comes from the associated privacy concerns during data transmission, storage and computation. To ensure privacy, this study implements a content-based image retrieval application for finding different types of brain tumors in the encrypted domain. In this framework, we propose a perceptual encryption technique to protect images in such a way that the features necessary for high-dimensional representation can still be extracted from the cipher images. Also, it allows data protection on the client side; therefore, the server stores and receives images in an encrypted form and has no access to the secret key information. Experimental results show that compared with conventional secure techniques, our proposed system reduced the difference in non-secure and secure retrieval performance by up to 3%.