Journal
DRIVING SCIENTIFIC AND ENGINEERING DISCOVERIES THROUGH THE INTEGRATION OF EXPERIMENT, BIG DATA, AND MODELING AND SIMULATION
Volume 1512, Issue -, Pages 139-156Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-030-96498-6_8
Keywords
Differential privacy; Secure multi-party computation; Secure sharing of scientific knowledge
Categories
Funding
- Laboratory Directed Research and Development (LDRD) program of Oak Ridge National Laboratory, under LDRD project [9831]
- Scientific User Facilities Division, Office of Basic Energy Sciences, U.S. Department of Energy
- DOE Office of Science User Facility [DE-AC05-00OR22725]
- Office of Science of the U.S. Department of Energy [DE-AC05-00OR22725]
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In a secure collaborative environment, tera-bytes of data generated from powerful scientific instruments are used to train secure machine learning models, which are then shared as cloud-based services with internal or external collaborators. This secure platform is necessary for seamless scientific knowledge sharing without compromising intellectual property and privacy. It enables new computing opportunities with sensitive data and accelerates scientific discovery.
In a secure collaborative environment, tera-bytes of data generated from powerful scientific instruments are used to train secure machine learning (ML) models on exascale computing systems, which are then securely shared with internal or external collaborators as cloud-based services. Devising such a secure platform is necessary for seamless scientific knowledge sharing without compromising individual, or institute-level, intellectual property and privacy details. By enabling new computing opportunities with sensitive data, we envision a secure collaborative environment that will play a significant role in accelerating scientific discovery. Several recent technological advancements have made it possible to realize these capabilities. In this paper, we present our efforts at ORNL toward developing a secure computation platform. We present a use case where scientific data generated from complex instruments, like those at the Spallation Neutron Source (SNS), are used to train a differential privacy enabled deep learning (DL) network on Summit, which is then hosted as a secure multi-party computation (MPC) service on ORNL's Compute and Data Environment for Science (CADES) cloud computing platform for third-party inference. In this feasibility study, we discuss the challenges involved, elaborate on leveraged technologies, analyze relevant performance results and present the future vision of our work to establish secure collaboration capabilities within and outside of ORNL.
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