TWS - Privacy-Preserving Machine Learning (2nd Offering) by: Ali Abbasi Tadi

Thursday, May 23, 2024 - 15:30

Technical Workshop Series

Privacy-Preserving Machine Learning (2nd Offering)

 

Presenter: Ali Abbasi Tadi

Date: Thursday, May 23rd, 2024

Time: 3:30 pm

Location: 4th Floor (Lecture space) at 300 Ouellette Avenue (School of Computer Science Advanced Computing Hub)

 

Abstract:
In today’s world, where machine learning (ML) has revolutionized all industries, the privacy of ML models has also increased; one of these concerns is the membership inference attack (MIA). MIA allows an adversary to query a trained machine-learning model to predict whether a particular example was contained in the model's training dataset. In this workshop, we detail MIA and determine the current mitigation techniques in the literature. This talk explores differential privacy, trusted execution environment, homomorphic encryption, and architectural approaches.
 
Workshop Outline:
  • Attacks that target machine learning models
  • Privacy-preserving Machine Learning techniques
  • What is MIA? And what are the reasons for that?
  • How can we mitigate MIA?
 
Prerequisites:
Neural Networks
 
Biography:
Ali is pursuing his Ph.D. in computer science at the University of Windsor. His main research interest is privacy-preserving machine learning. He has publications on private computing in top-tier conferences and peer-reviewed journals. He has received various scholarships from the University of Windsor and got 5th place in the iDash Security Competition 2022. He has been awarded for the best paper in Canadian AI 2022. He is developing a secure transformer framework for private computation in the cloud environment.

 

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