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Privacy in Machine Learning Systems with Data/Model Sharing via Information Theoretic Analysis
Privacy and Security are key considerations in any machine learning system. The proposed research will develop net methods for preserving privacy when training data and/or model parameters are released in the public domain. We will use methods from information theory to develop quantitative measures for privacy and security in machine learning systems. Using this framework we will study the performance of commonly used machine learning algorithms and develop new methods that enhance privacy and security while providing negligible loss in the accuracy of these systems. The research will be conducted in collaboration with Ericsson Research and will focus on both theoretical and practical topics.
Faculty Supervisor:
Ashish Khisti
Student:
Partner:
Ericsson Canada Inc (Quebec)
Discipline:
Engineering
Sector:
Information and cultural industries; Professional, scientific and technical services
University:
University of Toronto
Program:
Accelerate
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Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.