Two output papers of this project:
Nowadays is the big data era. A large amount of data are generated which can be valuable for business, healthcare, transportation, etc. To promote the dissemination of the valuable data, researchers have been trying to design and develop data sharing platforms. However, the existing platforms fail to address at least one of the three issues: trustworthiness, data heterogeneity, and authenticability. To this end, we propose TSAR, a fully-distributed Trustless data ShARing platform. In detail, we architect TSAR on Blockchain to remove the dependency on reliable third parties, which realizes the trustworthiness. Moreover, we propose a general data schema to represent raw data, which handles the problem of data heterogeneity. Finally, we record the data transaction as well as user-group information on Blockchain to achieve authenticability. To demonstrate the practicability and effectiveness of TSAR, we implement it in a minimal viable-product fashion and evaluate the performance in terms of throughput and response time.
Nowadays, a great number of healthcare data are generated every day from both medical institutions and individuals. Healthcare information exchange (HIE) has been proved to benefit the medical industry remarkably. To store and share such large amount of healthcare data is important while challenging. In this paper, we propose BlocHIE, a Blockchain-based platform for healthcare information exchange. First, we analyze the different requirements for sharing healthcare data from different sources. Based on the analysis, we employ two loosely-coupled Blockchains to handle different kinds of healthcare data. Second, we combine off-chain storage and on-chain verification to satisfy the requirements of both privacy and authenticability. Third, we propose two fairness-based packing algorithms to improve the system throughput and the fairness among users jointly. To demonstrate the practicability and effectiveness of BlocHIE, we implement BlocHIE in a minimal-viable-product way and evaluate the proposed packing algorithms extensively.