Presented at
Black Hat USA 2020 Virtual,
Aug. 6, 2020, 2:30 p.m.
(40 minutes).
<span>In recent years, deep-learning based side-channel attacks have been proven to be very effective and opened the door to automated implementation techniques. Building on this line of work, this talk explores how to take the approach a step further and showcases how to leverage the recent advance in AI explainability to quickly assess which parts of the implementation is responsible for the information. Through a concrete set by step example, we will showcase the promise of this approach, its limitations, and how it can be used today.</span>
Presenters:
-
Elie Bursztein
- Security & Anti-Abuse Research Lead, Google
Elie Bursztein leads Google's security & anti-abuse research, which helps protect users against Internet threats. His research focuses on advancing the state of applied-cryptography, machine learning for fraud and abuse, at risk user protections, and web security. He is the author of 60+ scholarly publications for which he received 6 best papers awards. Elie gave over 20 talks at leading industry conferences and received multiple industry awards including the Back Hat Pwnie award. He was invited to give over 20 guest lectures to numerous universities including Stanford, Berkeley and Tsing Hua. Elie's work is regularly covered by major news outlets including the Wall Street Journal, CBS, Forbes, Wired, the Huffington Post, and CNN. Elie is a beret aficionado, tweets at @elie, and performs magic tricks in his spare time. Born in Paris, he received a PhD from ENS-cachan in 2008 before working at Stanford University and ultimately joining Google in 2011. He now lives with his wife in Mountain View, California.
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