The scientific workflow is an important paradigm for automating and accelerating data processing and sharing in the scientific community. The correctness of scientific discoveries relies on the trustworthiness and reliability of the data processed by scientific workflows and the underlying cyberinfrastructure. Unfortunately, modern scientific workflow systems lack robust infrastructure support for the trustworthy execution of scientific workflows and for the protection of the data processed by such workflows. A scientist or student may forge or alter datasets or computation simply to get papers accepted for publication. A malicious user may also publish forged workflow data on websites, misleading other scientists into investigating and publishing invalid results. This project aims to support a community of engineers and scientists to collaboratively and securely collect, analyze, and share data using scientific workflows. The success of this project contributes significantly to the national cyberinfrastructure vision of securing the scientific discovery process for a wide range of science and engineering disciplines.
This project develops infrastructure support for secure execution of scientific workflows, detection of anomalous execution flows, and protection of scientific data. In particular, this project: (1) develops a trusted execution environment for scientific workflows leveraging the Intel Software Guard Extension (SGX) to protect the execution of scientific workflows as well as the data processed by scientific workflows; (2) produces encrypted, tamper-proof, and non-repudiable block-graphs that enable scientists to verify the origin of scientific data and examine how a piece of data was modified and distributed; and (3) develops a machine-learning based anomaly detection technique to detect anomalous execution flows based on logs collected by the underlying cyberinfrastructure.
