Keynotes: Main Conference
Democratizing Election Verification: New Methods for Addressing An Ancient Attacker Model
Prof. Vanessa Teague
Associate Professor (Adj.)
Australian National University, Thinking Cybersecurity Pty. Ltd., and Democracy Developers Ltd.
Rethinking IoT Security: Understanding and Mitigating Out-of-Band Vulnerabilities
Prof. Wenyuan Xu
These defects include unintentional coupling effects of sensors from ambient analog signals or abnormal channels that were not intentionally designed, collectively known as “out-of-band vulnerabilities.” Various security incidents have highlighted the prevalence of out-of-band vulnerabilities in IoT systems, and their activation can result in serious consequences.
Formal Methods for Payment Protocols
Prof. David Basin
Abstract: We report on experience using Tamarin, a security protocol model checker, to find numerous, serious exploitable vulnerabilities in EMV payment protocols. EMV is the international protocol standard for smartcard payment that is used in over 9 billion payment cards worldwide. Despite the standard’s advertised security, various issues have been previously uncovered, deriving from logical flaws that are hard to spot in EMV’s lengthy and complex specification, running over 2,000 pages.
We have formalized a comprehensive model of EMV in Tamarin. We use our model to automatically discover new flaws that lead to critical attacks on EMV. In particular, an attacker can use a victim’s EMV card (e.g., Mastercard or Visa Card) for high-valued purchases without the victims PIN. Said more simply, the PIN on your EMV card is useless! We describe these attacks, their repair, and more generally why using formal methods is essential for critical protocols like payment protocols.
Speaker’s Bio: David Basin is a full professor of Computer Science at ETH Zurich, since 2003. His research areas are Information Security and Software Engineering. He is the founding director of the ZISC, the Zurich Information Security Center, which he led from 2003-2011. He served as Editor-in-Chief of the ACM Transactions on Privacy and Security (2015-2020) and of Springer-Verlag’s book series on Information Security and Cryptography (2008-present). He has co-founded three security companies, is on the board of directors of Anapaya Systems AG, and on various management and scientific advisory boards. He is an IEEE Fellow and an ACM Fellow.
Model Stealing Attacks and Defenses: Where are we now?
Prof. N. Asokan
Professor and David R. Cheriton Chair
The University of Waterloo
Abstract: The success of deep learning in many application domains has been nothing short of dramatic. This has brought the spotlight onto security and privacy concerns with machine learning (ML). One such concern is the threat of model theft. I will discuss work on exploring the threat of model theft, especially in the form of “model extraction attacks” — when a model is made available to customers via an inference interface, a malicious customer can use repeated queries to this interface and use the information gained to construct a surrogate model.
I will also discuss possible countermeasures, focusing on deterrence mechanisms that allow for model ownership resolution (MOR) based on watermarking or fingerprinting. In particular, I will discuss the robustness of MOR schemes. I will touch on the issue of conflicts that arise when protection mechanisms for multiple different threats need to be applied simultaneously to a given ML model, using MOR techniques as a case study.
This talk is based on work done with my students and collaborators, including Buse Atli Tekgul, Jian Liu, Mika Juuti, Rui Zhang, Samuel Marchal, and Sebastian Szyller. The work was funded in part by Intel Labs in the context of the Private AI consortium.
Speaker’s Bio: N. Asokan is a professor of computer science and a David R. Cheriton Chair at the University of Waterloo where he also serves as the executive director of the Cybersecurity and Privacy Institute. Asokan is an ACM Fellow and an IEEE Fellow. More information about his work is on his website at https://asokan.org/asokan/.