Secure Collaborative Environment for Seamless Sharing of Scientific Knowledge

Srikanth Yoginath; Mathieu Doucet; Debsindhu Bhowmik; David Heise; Folami Alamudun; Hong Jun Yoon; Christopher Stanley

doi:10.1007/978-3-030-96498-6_8

Secure Collaborative Environment for Seamless Sharing of Scientific Knowledge

Srikanth Yoginath, Mathieu Doucet, Debsindhu Bhowmik, David Heise, Folami Alamudun, Hong Jun Yoon, Christopher Stanley

Oak Ridge National Laboratory

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation - 21st Smoky Mountains Computational Sciences and Engineering, SMC 2021, Revised Selected Papers
Editors	[given-name]Jeffrey Nichols, [given-name]Arthur ‘Barney’ Maccabe, James Nutaro, Swaroop Pophale, Pravallika Devineni, Theresa Ahearn, Becky Verastegui
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	139-156
Number of pages	18
ISBN (Print)	9783030964979
DOIs	https://doi.org/10.1007/978-3-030-96498-6_8
State	Published - 2022
Event	21st Smoky Mountains Computational Sciences and Engineering Conference, SMC 2021 - Virtual, Online Duration: Oct 18 2021 → Oct 20 2021

Publication series

Name	Communications in Computer and Information Science
Volume	1512 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	21st Smoky Mountains Computational Sciences and Engineering Conference, SMC 2021
City	Virtual, Online
Period	10/18/21 → 10/20/21

Bibliographical note

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.

Funding

Acknowledgements. This work was supported by the Laboratory Directed Research and Development (LDRD) program of Oak Ridge National Laboratory, under LDRD project 9831. A portion of this research at ORNL’s Spallation Neutron Source was sponsored by the Scientific User Facilities Division, Office of Basic Energy Sciences, U.S. Department of Energy. C.S. acknowledges the EQ-SANS beamline staff: Changwoo Do, Carrie Gao, and William Heller, that also assisted in the calibration samples data collection over the time period. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. This research used resources of the Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. We would like to acknowledge the timely support and assistance provided by Chris Layton and Daniel Dewey. We very much appreciate their help and support. Keywords: Differential privacy · Secure multi-party computation · Secure sharing of scientific knowledge Notice of Copyright This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/ downloads/doe-public-access-plan). This work was supported by the Laboratory Directed Research and Development (LDRD) program of Oak Ridge National Laboratory, under LDRD project 9831. A portion of this research at ORNL’s Spallation Neutron Source was sponsored by the Scientific User Facilities Division, Office of Basic Energy Sciences, U.S. Department of Energy. C.S. acknowledges the EQ-SANS beamline staff: Changwoo Do, Carrie Gao, and William Heller, that also assisted in the calibration samples data collection over the time period. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. This research used resources of the Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. We would like to acknowledge the timely support and assistance provided by Chris Layton and Daniel Dewey. We very much appreciate their help and support.

Funders	Funder number
CADES
Data Environment for Science
Notice of Copyright
Scientific User Facilities Division
William Heller
U.S. Department of Energy
Office of Science	DE-AC05-00OR22725
Basic Energy Sciences
Laboratory Directed Research and Development	9831

Keywords

Differential privacy
Secure multi-party computation
Secure sharing of scientific knowledge

Access to Document

10.1007/978-3-030-96498-6_8

Cite this

Yoginath, S., Doucet, M., Bhowmik, D., Heise, D., Alamudun, F., Yoon, H. J., & Stanley, C. (2022). Secure Collaborative Environment for Seamless Sharing of Scientific Knowledge. In J. Nichols, A. B. Maccabe, J. Nutaro, S. Pophale, P. Devineni, T. Ahearn, & B. Verastegui (Eds.), Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation - 21st Smoky Mountains Computational Sciences and Engineering, SMC 2021, Revised Selected Papers (pp. 139-156). (Communications in Computer and Information Science; Vol. 1512 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-96498-6_8

Yoginath, Srikanth ; Doucet, Mathieu ; Bhowmik, Debsindhu et al. / Secure Collaborative Environment for Seamless Sharing of Scientific Knowledge. Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation - 21st Smoky Mountains Computational Sciences and Engineering, SMC 2021, Revised Selected Papers. editor / [given-name]Jeffrey Nichols ; [given-name]Arthur ‘Barney’ Maccabe ; James Nutaro ; Swaroop Pophale ; Pravallika Devineni ; Theresa Ahearn ; Becky Verastegui. Springer Science and Business Media Deutschland GmbH, 2022. pp. 139-156 (Communications in Computer and Information Science).

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abstract = "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{\textquoteright}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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Yoginath, S , Doucet, M , Bhowmik, D , Heise, D, Alamudun, F, Yoon, HJ & Stanley, C 2022, Secure Collaborative Environment for Seamless Sharing of Scientific Knowledge. in J Nichols, AB Maccabe, J Nutaro, S Pophale, P Devineni, T Ahearn & B Verastegui (eds), Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation - 21st Smoky Mountains Computational Sciences and Engineering, SMC 2021, Revised Selected Papers. Communications in Computer and Information Science, vol. 1512 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 139-156, 21st Smoky Mountains Computational Sciences and Engineering Conference, SMC 2021, Virtual, Online, 10/18/21. https://doi.org/10.1007/978-3-030-96498-6_8

Secure Collaborative Environment for Seamless Sharing of Scientific Knowledge. / Yoginath, Srikanth ; Doucet, Mathieu ; Bhowmik, Debsindhu et al.
Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation - 21st Smoky Mountains Computational Sciences and Engineering, SMC 2021, Revised Selected Papers. ed. / [given-name]Jeffrey Nichols; [given-name]Arthur ‘Barney’ Maccabe; James Nutaro; Swaroop Pophale; Pravallika Devineni; Theresa Ahearn; Becky Verastegui. Springer Science and Business Media Deutschland GmbH, 2022. p. 139-156 (Communications in Computer and Information Science; Vol. 1512 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Yoginath S , Doucet M , Bhowmik D , Heise D, Alamudun F, Yoon HJ et al. Secure Collaborative Environment for Seamless Sharing of Scientific Knowledge. In Nichols J, Maccabe AB, Nutaro J, Pophale S, Devineni P, Ahearn T, Verastegui B, editors, Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation - 21st Smoky Mountains Computational Sciences and Engineering, SMC 2021, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2022. p. 139-156. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-96498-6_8

Secure Collaborative Environment for Seamless Sharing of Scientific Knowledge

Abstract

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