USENIX Security 2018

Three SRG posters were presented at USENIX Security Symposium 2018 in Baltimore, Maryland: Nathaniel Grevatt (GDPR-Compliant Data Processing: Improving Pseudonymization with Multi-Party Computation) Matthew Wallace and Parvesh Samayamanthula (Deceiving Privacy Policy Classifiers with Adversarial Examples) Guy Verrier (How is GDPR Affecting Privacy Policies?, joint with Haonan Chen and Yuan Tian) There were also a surprising number of appearances by an unidentified unicorn:

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Mutually Assured Destruction and the Impending AI Apocalypse

I gave a keynote talk at USENIX Workshop of Offensive Technologies, Baltimore, Maryland, 13 August 2018. The title and abstract are what I provided for the WOOT program, but unfortunately (or maybe fortunately for humanity!) I wasn’t able to actually figure out a talk to match the title and abstract I provided. The history of security includes a long series of arms races, where a new technology emerges and is subsequently developed and exploited by both defenders and attackers.

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Cybersecurity Summer Camp

I helped organize a summer camp for high school teachers focused on cybersecurity, led by Ahmed Ibrahim. Some of the materials from the camp on cryptography, including the Jefferson Wheel and visual cryptography are here: Cipher School for Muggles. Cybersecurity Goes to Summer Camp. UVA Today. 22 July 2018. [] Earlier this week, 25 high school teachers – including 21 from Virginia – filled a glass-walled room in Rice Hall, sitting in high adjustable chairs at wheeled work tables, their laptops open, following a lecture with graphics about the dangers that lurk in cyberspace and trying to figure out how to pass the information on to a generation that seems to share the most intimate details of life online.

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Dependable and Secure Machine Learning

I co-organized, with Homa Alemzadeh and Karthik Pattabiraman, a workshop on trustworthy machine learning attached to DSN 2018, in Luxembourg: DSML: Dependable and Secure Machine Learning.

DLS Keynote: Is 'adversarial examples' an Adversarial Example?

I gave a keynote talk at the 1st Deep Learning and Security Workshop (co-located with the 39th IEEE Symposium on Security and Privacy). San Francisco, California. 24 May 2018 Abstract Over the past few years, there has been an explosion of research in security of machine learning and on adversarial examples in particular. Although this is in many ways a new and immature research area, the general problem of adversarial examples has been a core problem in information security for thousands of years.

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Wahoos at Oakland

UVA Group Dinner at IEEE Security and Privacy 2018

Including our newest faculty member, Yongwhi Kwon, joining UVA in Fall 2018!

Yuan Tian, Fnu Suya, Mainuddin Jonas, Yongwhi Kwon, David Evans, Weihang Wang, Aihua Chen, Weilin Xu

Poster Session

Fnu Suya (with Yuan Tian and David Evans), Adversaries Don’t Care About Averages: Batch Attacks on Black-Box Classifiers [PDF]

Mainuddin Jonas (with David Evans), Enhancing Adversarial Example Defenses Using Internal Layers [PDF]

Lessons from the Last 3000 Years of Adversarial Examples

I spoke on Lessons from the Last 3000 Years of Adversarial Examples at Huawei’s Strategy and Technology Workshop in Shenzhen, China, 15 May 2018. We also got to tour Huawei’s new research and development campus, under construction about 40 minutes from Shenzhen. It is pretty close to Disneyland, with its own railroad and villages themed after different European cities (Paris, Bologna, etc.). Huawei’s New Research and Development Campus [More Pictures]

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Feature Squeezing at NDSS

Weilin Xu presented Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks at the Network and Distributed System Security Symposium 2018. San Diego, CA. 21 February 2018.

Paper: Weilin Xu, David Evans, Yanjun Qi. Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks. NDSS 2018. [PDF]

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