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Title: Secure Collaborative Environment for Seamless Sharing of Scientific Knowledge

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 securemore » collaboration capabilities within and outside of ORNL.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1863310
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Journal Volume: 1512; Conference: Smoky Mountains Computational Sciences and Engineering Conference (SMC) - Kingsport, Tennessee, United States of America - 10/18/2021 8:00:00 AM-10/20/2021 8:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Yoginath, Srikanth, Doucet, Mathieu, Bhowmik, Debsindhu, Heise, David, Alamudun, Folami, Yoon, Hong-Jun, and Stanley, Christopher. Secure Collaborative Environment for Seamless Sharing of Scientific Knowledge. United States: N. p., 2022. Web. doi:10.1007/978-3-030-96498-6_8.
Yoginath, Srikanth, Doucet, Mathieu, Bhowmik, Debsindhu, Heise, David, Alamudun, Folami, Yoon, Hong-Jun, & Stanley, Christopher. Secure Collaborative Environment for Seamless Sharing of Scientific Knowledge. United States. https://doi.org/10.1007/978-3-030-96498-6_8
Yoginath, Srikanth, Doucet, Mathieu, Bhowmik, Debsindhu, Heise, David, Alamudun, Folami, Yoon, Hong-Jun, and Stanley, Christopher. 2022. "Secure Collaborative Environment for Seamless Sharing of Scientific Knowledge". United States. https://doi.org/10.1007/978-3-030-96498-6_8. https://www.osti.gov/servlets/purl/1863310.
@article{osti_1863310,
title = {Secure Collaborative Environment for Seamless Sharing of Scientific Knowledge},
author = {Yoginath, Srikanth and Doucet, Mathieu and Bhowmik, Debsindhu and Heise, David and Alamudun, Folami and Yoon, Hong-Jun and Stanley, Christopher},
abstractNote = {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.},
doi = {10.1007/978-3-030-96498-6_8},
url = {https://www.osti.gov/biblio/1863310}, journal = {},
issn = {1865--0929},
number = ,
volume = 1512,
place = {United States},
year = {2022},
month = {3}
}

Conference:
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