Hacking AI: Security & Privacy of Machine Learning Models

May 11, 2021 | 10:00-11:00 am PDT

Summary

Machine learning has enabled dramatic advances in many areas, including cybersecurity. But, it also raises important security and privacy concerns. Malicious actors can fool machine learning algorithms. Attackers can poison an entire training process by corrupting one item in a large data set. Models can leak the underlying training data.

In this webinar, Professor Dan Boneh will discuss recent work at the intersection of cybersecurity and machine learning. Specifically, he will explore an area known as “adversarial machine learning” which looks at the stability of machine learning models in the presence of adversarial behavior.

You Will Learn:
  • What recent research on adversarial behavior tells us about machine learning models
  • How to protect classification and training processes from attacks
  • Ways to insure the privacy of underlying training data
  • Item 4
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[email protected] | 650-204-3984

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Dan Boneh

Professor Boneh heads the applied cryptography group and co-directs the computer security lab. Professor Boneh's research focuses on applications of cryptography to computer security. His work includes cryptosystems with novel properties, web security, security for mobile devices, and cryptanalysis. He is a recipient of multiple awards such as the 2014 ACM prize and the 2013 Godel prize, and the Ishii award for industry education innovation. Professor Boneh received his Ph.D. from Princeton University and joined Stanford in 1997.

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