SciChain: Blockchain-enabled Lightweight and Efficient Data Provenance for Reproducible Scientific Computing

The state-of-the-art for auditing and reproducing scientific applications on high-performance computing (HPC) systems is through a data provenance subsystem. While recent advances in data provenance lie in reducing the performance overhead and improving the user’s query flexibility, the fidelity of data provenance is often overlooked: there is no such way to ensure that the provenance data itself has not been fabricated or falsified. This paper advocates leveraging blockchains to deliver immutable and autonomous data provenance services such that scientific discoveries are trustworthy. The challenges for adopting blockchains to HPC include designing a new blockchain architecture compatible with the HPC platforms and, more importantly, a set of new consensus protocols for scientific applications atop blockchains. To this end, we have designed the proof-of-scalable-traceability (POST) protocol and implemented it in a blockchain prototype, namely SciChain, the very first practical blockchain system for provenance services on HPC. We evaluated SciChain by comparing it with multiple state-of-the-art systems; experimental results showed that SciChain guaranteed trustworthy data provenance while incurring orders of magnitude lower overhead than existing solutions.