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The healthcare sector is constantly improving patient health record systems. However, these systems face a significant challenge when confronted with patient health record (PHR) data due to its sensitivity. In addition, patient’s data is stored and spread generally across various healthcare facilities and among providers. This arrangement of distributed data becomes problematic whenever patients want to access their health records and then share them with their care provider, which yields a lack of interoperability among various healthcare systems. Moreover, most patient health record systems adopt a centralized management structure and deploy PHRs to the cloud, which raises privacy concerns when sharing patient information over a network. Therefore, it is vital to design a framework that considers patient privacy and data security when sharing sensitive information with healthcare facilities and providers. This paper proposes a blockchain framework for secured patient health records sharing that allows patients to have full access and control over their health records. With this novel approach, our framework applies the Ethereum blockchain smart contracts, the Inter-Planetary File System (IPFS) as an off-chain storage system, and the NuCypher protocol, which functions as key management and blockchain-based proxy re-encryption to create a secured on-demand patient health records sharing system effectively. Results show that the proposed framework is more secure than other schemes, and the PHRs will not be accessible to unauthorized providers or users. In addition, all encrypted data will only be accessible to and readable by verified entities set by the patient.
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Invited Talk 2: Techniques for Automating Assessment of Parallel Programming Assignments
This talk will present a design space based on these dimensions using concrete examples of several systems/frameworks including Gradescope, Web-CAT, the JUnit testing framework, diff-based systems, and a system we have developed at UNC. It will point out future directions that can be pursued to develop a software framework for assessing concurrent programs written in multiple programming languages that improves the productivity and learning, respectively, of trainers and trainees.
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- Award ID(s):
- 1924059
- PAR ID:
- 10190354
- Date Published:
- Journal Name:
- 2019 26th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW)
- Page Range / eLocation ID:
- 38 to 38
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/HiPCW.2019.00018
