Grants and Contributions:
Title:
An empirical framework for robust machine learning systems and its application in AI for logistics
Agreement Number:
1000536
Agreement Value:
$263,793.00
Agreement Date:
Mar 30, 2023 - Mar 31, 2026
Description:
Machine Learning (ML) technologies have been widely adopted in many
mission-critical fields to support intelligent decision-making with superior
performance. With the success of these new technologies, the application of
ML introduces novel and significant threats to AI-powered systems.
Policymakers around the world have made a number of ongoing efforts on
regulation enactment to enforce and normalize AI cybersecurity and privacy.
It is essential to ensure that ML systems can achieve regulatory compliance
and satisfy the standard requirements.
This project will focus on developing a taxonomy of state-of-the-art ML
offensive/defensive technologies based on a comprehensive literature
review, including a collection of open-source adversarial challenges and
defense utilities; devising efficient security and privacy defense mechanisms
against the threats faced in the ML model training and prediction phase; and
developing an empirical framework consisting of a toolset of best practices
that can be leveraged to enable robust ML application development and
deployment.
Organization:
National Research Council Canada
Expected Results:
In the short term, anticipated outcomes will be strengthened collaborations across industry, academia, and government to support research excellence. In the medium term, anticipated outcomes will be the development of new and potentially disruptive technologies with collaborators. In the long term, find collaborative solutions to public policy challenges and create stronger innovation systems.
Location:
Guelph, Ontario, CA N1G 2W1
Reference Number:
172-2022-2023-Q4-1000536
Agreement Type:
Grant
Report Type:
Grants and Contributions
Recipient Business Number:
108161829
Recipient Type:
Academia
Additional Information:
This agreement has been amended 1 time(s). The total amended value is $263,793.
Amendment Date
Feb 7, 2025
Recipient's Legal Name:
University of Guelph
Federal Riding Name:
Guelph
Federal Riding Number:
35033
Program:
Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives
Program Purpose:
Collaborate on multiparty research and development programs to catalyze transformative, high-risk, high-reward research with the potential for game-changing scientific discoveries and technological breakthroughs in priority areas.
NAICS Code:
541710
Amendments: