Background: A computer tomography image (CI) sequence can be regarded as a time-series data that is composed of a great deal of nearby and similar CIs. Since the computational and I/O costs of similarity measure, encryption, and decryption calculation during a similarity retrieval of the large CI sequences (CIS) are extremely high, deploying all retrieval tasks in the cloud, however, will lead to excessive computing load on the cloud, which will greatly and negatively affect the retrieval performance.
Methodologies: To tackle the above challenges, the paper proposes a progressive privacy-preserving Batch Retrieval scheme for the lung CISs based on edge-cloud collaborative computation called the BRS method. There are four supporting techniques to enable the BRS method, such as: 1) batch similarity measure for CISs, 2) CIB-based privacy preserving scheme, 3) uniform edge-cloud index framework, and 4) edge buffering.
Results: The experimental results reveal that our method outperforms the state-of-the-art approaches in terms of efficiency and scalability, drastically reducing response time by lowering network communication costs while enhancing retrieval safety and accuracy.