Blockchain-based Secure Storage and Cross-domain Sharing Mechanism for Medical Image Data
With the rapid advancements in medical imaging technologies such as CT, MRI, PET, and ultrasound, these modalities have become pivotal for precise clinical diagnoses and treatment planning. Notably, they facilitate accurate patient evaluations and expedite early disease detection and targeted interventions. However, the voluminous nature of medical image data, coupled with stringent privacy requirements, poses significant challenges to efficient and secure storage and sharing within the realm of big data management. In light of these complexities, this paper introduces a blockchain-centric framework that innovatively integrates InterPlanetary File System (IPFS), cloud storage, AES and blockchain technologies. The framework is meticulously designed to address the dual objectives of secure storage and streamlined sharing of medical image data. A key novelty lies in the strategic differentiation of cloud service roles: the cloud's “chain-on” nodes participate in blockchain's validation and consensus processes without engaging in direct data management or operations, whereas “chain-off” nodes are tasked with establishing transient channels for data transmission upon successful validation of user requests by their chain-on counterparts. In practice, medical image owners encrypt the data using IPFS and create indexes derived from examination reports, facilitating keyword-based searches for data users. Moreover, a Hybrid Encryption scheme with Multiple Public Keys is implemented, necessitating users to utilize their blockchain management system-generated private keys to unlock the AES decryption key for image data access. This multi-faceted technology integration approach not only fortifies the security of storage and ensures privacy preservation but also streamlines retrieval processes and bolsters permission management for medical image data. Therefore, it presents a pioneering solution for medical image data storage and sharing.
Authors
- Chenguang Wang
- Chuanjia Yao
- Leijin Long
- Rong Jiang (ORCID: https://orcid.org/0000-0001-8873-6810)
- J. Dong (ORCID: https://orcid.org/0009-0003-8913-9104)
Institutions
- Kunming University of Science and Technology (CN)
- Yunnan University of Finance And Economics (CN)
Publication Details
- Published
- 2024-08-15
- DOI
- https://doi.org/10.1145/3694860.3694865
- Citations
- 3
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.74