A Privacy-Preserving Framework Using Hyperledger Fabric for EHR Sharing Applications

Electronic Health Records, or EHRs, include private and sensitive information of a patient. The privacy of personal healthcare data can be protected through Hyperledger Fabric, a permissioned blockchain framework. A few Hyperledger Fabric- integrated EHR solutions have emerged in recent years. However, none of them implements the privacy-preserving techniques of Hyperledger Fabric to make transactions anonymous or preserve the transaction data privacy during the consensus. Our proposed architecture is built on Hyperledger Fabric and its privacy-preserving mechanisms, such as Identity Mixer, Private Data Collections, Channels and Transient Fields to securely store and transfer patient-sensitive data while providing anonymity and unlinkability of transactions

Using Blockchain Technology for The Organ Procurement and Transplant Network

The organ donation system in the United States is centralized and difficult to audit by the general public. This centralized approach may lead to data integrity issues in the future. The Organ Procurement and Transplant Network (OPTN) was built and maintained by a non-governmental organization called the United Network for Organ Sharing (UNOS) under its proprietary UNet(SM) umbrella platform. This platform is made up of proprietary closed source software and does not provide the general public easy access to the organ transplant data for auditing. This study investigates the feasibility, challenges, and advantages of a blockchain-based OPTN. A prototype of a blockchain-based OPTN was created using the Hyperledger Fabric framework. The policies and guidelines issued by the United States Department of Health and Human Services for UNOS and the OPTN were used as the basis of this prototype. Four factors were identified to have a direct effect on the performance of this system, viz. max batch time out, max block size, endorsement policy, and transaction rate. Additionally, two variants of the blockchain chaincode were also developed. The first variant performed the organ-candidate matching inside the blockchain (Scheme A), and the second variant performed it outside the blockchain (Scheme B). Analysis of these data showed that Scheme A outperformed Scheme B in all experiments for write-operations. However, the read operations remained unaffected by any of the experiment variables in the given environment. Based on these results, it is recommended to perform the organ-candidate matching on the blockchain with the max batch time out close to the transaction rate