NeurIPS 2018: Distributed Learning without Distress

Bargav Jayaraman presented our work on privacy-preserving machine learning at the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018) in Montreal. Distributed learning (sometimes known as federated learning) allows a group of independent data owners to collaboratively learn a model over their data sets without exposing their private data. Our approach combines differential privacy with secure multi-party computation to both protect the data during training and produce a model that provides privacy against inference attacks.

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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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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. [archive.org] 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.

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]
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