Privacy or Performance? Towards Secure and Scalable Medical Image Storage and Retrieval Management in the Cloud

Medical images play an important role in diagnosis in digitalized healthcare and carry essential and sensitive information regarding effective patient care.Given the increasing volume of medical imaging data, the healthcare sector has become more reliant on cloud-based third-party storage for the purposes of effective storage and retrieval.Content-Based Medical Image Retrieval (CBMIR) has become important.However, privacy and security concerns are inherent to cloud-based solutions.This thesis has laid out a series of frameworks from different points of view to provide secure, efficient, and scalable storage and retrieval of medical images.An integrity-centric image encryption technique has been developed to ensure better security along with searchable encrypted hashcodes that can help with CBMIR.Nevertheless, achieving a balance between retrieval performance and security remains a challenge.Targeted encryption is employed to achieve this balance by focusing on sensitive regions in the images, thus exploiting fusion-based hashcodes to assist with image retrieval.For scaling purposes, a distributed storage scheme is presented, where the encrypted images are broken down into shadow segments to ensure data integrity and resilience.Edge computing is used to achieve better latency with security and scalability.The presented solutions for secure storage and retrieval of medical images provide novel frameworks to support the healthcare industry's demands in a cloud environment, making these solutions relevant for improving security, performance, and scale in digital healthcare applications.