Abstract
Multi-site clinical trial systems face security challenges when streamlining information sharing while protecting patient privacy. In addition, patient enrollment, transparency, traceability, data integrity, and reporting in clinical trial systems are all critical aspects of maintaining data compliance. A Blockchain-based clinical trial framework has been proposed by lots of researchers and industrial companies recently, but its limitations of lack of data governance, limited confidentiality, and high communication overhead made data-sharing systems insecure and not efficient.
We propose \(\textsf{Soteria}\), a privacy-preserving smart contracts framework, to manage, share and analyze clinical trial data on fabric private chaincode (FPC). Compared to public Blockchain, fabric has fewer participants with an efficient consensus protocol. \(\textsf{Soteria}\) consists of several modules: patient consent and clinical trial approval management chaincode, secure execution for confidential data sharing, API Gateway, and decentralized data governance with adaptive threshold signature (ATS). We implemented two versions of \(\textsf{Soteria}\) with non-SGX deploys on AWS blockchain and SGX-based on a local data center. We evaluated the response time for all of the access endpoints on AWS Managed Blockchain, and demonstrated the utilization of SGX-based smart contracts for data sharing and analysis.
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Notes
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An individual who conducts a clinical investigation.
- 2.
Regulatory compliance is an organization’s adherence to laws, regulations, guidelines, and specifications relevant to its business processes.
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Acknowledgement
We gratefully acknowledge the support of the NSF through grant IIP-1919159. We also acknowledge the support of Andrew Weiss, and Mic Bowman from Intel.
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Wu, Y., Liu, C., Sebald, L., Nguyen, P., Yesha, Y. (2023). Apply Trust Computing and Privacy Preserving Smart Contracts to Manage, Share, and Analyze Multi-site Clinical Trial Data. In: Awan, I., Younas, M., Bentahar, J., Benbernou, S. (eds) The International Conference on Deep Learning, Big Data and Blockchain (DBB 2022). DBB 2022. Lecture Notes in Networks and Systems, vol 541. Springer, Cham. https://doi.org/10.1007/978-3-031-16035-6_1
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