An Accurate and Privacy-Preserving Retrieval Scheme Over Outsourced Medical Images

With the rapid advancement in medical imaging techniques, Content-Based (medical) Image Retrieval (CBIR), which can assist in disease diagnosis, has gained much attention in both academia and industry. However, due to patients' sensitive information involved in medical images, privacy-preserving CBIR is a challenge worth exploiting. Though several privacy-preserving CBIR schemes have been put forth, they can only resist known-background attack (KBA), and do not suffice for protecting the image privacy in outsourced settings. In this article, aiming at the above challenge, we first design a novel Privacy-preserving Mahalanobis Distance Comparison (PMDC) method to improve the accuracy of medical images retrieval. Then, combined with the Mahalanobis distance based Fuzzy C-Means (FCM-M) algorithm, a scheme named TAMMIE is proposed to achieve accurate and privacy-preserving medical image retrieval over encrypted data. With TAMMIE, an image owner can securely outsource the images and indexes to a cloud server, and query users can request retrieval services from the cloud server while keeping their queries private. Detailed security analysis shows that our proposed schemes are secure under the attack stronger than KBA. Furthermore, thorough empirical experiments conducted on two real-world and one synthetic datasets also demonstrate the efficiency of TAMMIE.