School of Computer Science
Technical Workshop Series: Secure Programming with Intel SGX
Presenter: Ali Abbasi Tadi, Ph.D. Candidate
Date: Friday, November 24th, 2023
Time: 3:00 pm - 4:00pm
Location: 4th Floor (Workshop space) at 300 Ouellette Avenue (School of Computer Science Advanced Computing Hub)
LATECOMERS WILL NOT BE ADMITTED once the presentation has begun.
In today’s world, where artificial intelligence has revolutionized every industry, the concerns about using AI models have increased as well. One of these concerns is privacy in machine learning as a service. There is a challenge to find a way to do computations of machine learning in a cloud environment in a secure way. Intel Software Guard eXtension (SGX) is an ideal tool for doing secure programming for machine learning. SGX allows private programming so that only the trusted entities can access the code and data of the training model. This talk explores how to design a trusted program using Linux SGX SDK. We investigate the concepts of Intel SGX data structure as well as sealing and attestation in the SGX environment. Also, we provide examples to do secure programming without any unauthorized access from OS or a third party to the data.
The main idea of Intel SGX
How Intel SGX works.
What is sealing?
What is attestation?
How to design a simple secure program using Intel SGX
Shell scripting, C/C++ programming
Ali is pursuing his Ph.D. in computer science at the University of Windsor. His main research interest is security/privacy in 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 2022 competition. He has been invited as a speaker at the Advanced Computing Hub at the University of Windsor. He is serving as the program committee and editorial board of top-tier conferences and journals. He is currently developing a secure framework for highly qualified parallel clustering for sensitive genomic data.