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Privacy-Preserving Machine Learning: Strategies and Tools

Offered By: Toronto Machine Learning Series (TMLS) via YouTube

Tags

Privacy-Preserving Machine Learning Courses Machine Learning Courses Federated Learning Courses Data Privacy Courses Differential Privacy Courses Homomorphic Encryption Courses Trusted Execution Environment Courses Secure Multiparty Computation Courses

Course Description

Overview

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Explore privacy-preserving machine learning techniques in this 39-minute conference talk by Patricia Thaine, CEO and PhD Candidate at Private AI, University of Toronto. Gain strategic insights into implementing privacy tools such as federated learning, homomorphic encryption, differential privacy, anonymization/pseudonymization, secure multiparty computation, and trusted execution environments. Learn how to evaluate and combine these tools to achieve specific privacy goals while considering risk factors, implementation complexity, and computational resource requirements. Discover practical examples that illustrate how to approach privacy challenges in machine learning pipelines, equipping you with the knowledge to make informed decisions when developing privacy-preserving AI systems.

Syllabus

Patricia Thaine - Privacy-Preserving Machine Learning


Taught by

Toronto Machine Learning Series (TMLS)

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