Grants and Contributions:

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Title:

An empirical framework for robust machine learning systems and its application in AI4L

Agreement Number:

1000543

Agreement Value:

$281,765.00

Agreement Date:

Mar 28, 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:

Burnaby, British Columbia, CA V5A 1S6

Reference Number:

172-2022-2023-Q4-1000543

Agreement Type:

Grant

Report Type:

Grants and Contributions

Recipient Business Number:

118520725

Recipient Type:

Academia

Recipient's Legal Name:

SIMON FRASER UNIVERSITY

Federal Riding Name:

Burnaby North–Seymour

Federal Riding Number:

59002

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