Ph.D. Student
Department of Computer Science
University of Chicago

Google Scholar, CV

Contact
Email: shawnshan@cs.uchicago.edu
Twitter: @shawnshan26
Linkedin: shawn-shan
Github: Shawn-Shan


I am a Ph.D. student at University of Chicago. I work in the SAND Lab, co-advised by Professor Ben Y. Zhao and Professor Heather Zheng.

My research lies in the intersection of machine learning and security and privacy. Currently, I am exploring the limitations, vulnerabilities, and privacy implications of neural networks. Some of my recent works include protecting neural networks from backdoor and adversarial attacks, using imperceptible perturbation to protect user privacy, etc.

I received my BS in computer science from University of Chicago in 2020. I have spent two summers at Facebook as a software engineer.

Follow me on Twitter at @shawnshan26.


Latest Updates

Nov 2020: I presented our honeypot adversarial defense paper at CCS'20. We made the source code public on Github.
Jul 2020: We released our Fawkes software for Mac and Windows. Checkout more at our project website!

Preprints

  • A Real-time Defense against Website Fingerprinting Attacks
    Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng, Ben Y. Zhao
    In submission
    Preprint

  • Blacklight: Defending Black-Box Adversarial Attacks on Deep Neural Networks
    Huiying Li, Shawn Shan, Emily Wenger, Jiayun Zhang, Haitao Zheng, and Ben Y. Zhao.
    In submission
    Preprint

  • Piracy Resistant Watermarks for Deep Neural Networks
    Huiying Li, Emily Wenger, Shawn Shan, Ben Y. Zhao, Haitao Zheng
    In submission
    Preprint

Publications

  • Deep Entity Classification: Abusive Account Detection for Online Social Networks
    Teng Xu, Gerard Goossen, Huseyin Kerem Cevahir, Sara Khodeir, Yingyezhe Jin, Frank Li, Shawn Shan, Sagar Patel, David Freeman, Paul Pearce
    To Appear at USENIX Security Symposium (USENIX Security'21)
    PDF

  • Gotta Catch’Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks
    Shawn Shan, Emily Wenger, Bolun Wang, Bo Li, Haitao Zheng, and Ben Y. Zhao.
    ACM SIGSAC Conference on Computer and Communications Security (CCS'20), November 2020
    PDF Slides Code Video

  • Fawkes: Protecting Personal Privacy against Unauthorized Deep Learning Models
    Shawn Shan, Emily Wenger, Jiayun Zhang, Huiying Li, Haitao Zheng, and Ben Y. Zhao.
    USENIX Security Symposium (USENIX Security'20), August 2020
    PDF Webpage Code Video

  • Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks
    Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y. Zhao
    IEEE Symposium on Security and Privacy (SP'19), May 2019
    PDF Slides Code

  • Oh, the Places You've Been! User Reactions to Longitudinal Transparency About Third-Party Web Tracking and Inferencing
    Ben Weinshel, Miranda Wei, Mainack Mondal, Euirim Choi, Shawn Shan, Claire Dolin, Michelle L. Mazurek, and Blase Ur
    ACM SIGSAC Conference on Computer and Communications Security (CCS'19), November 2019
    PDF Code

  • Unpacking Perceptions of Data-Driven Inferences Underlying Online Targeting and Personalization
    Claire Dolin, Ben Weinshel, Shawn Shan, Chang Min Hahn, Euirim Choi, Michelle L. Mazurek, Blase Ur.
    ACM SIGCHI Conference on Human Factors in Computing Systems (CHI'18), April 2018
    PDF

Selected News