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Topics of Controversy: An Empirical Analysis of Web Censorship Lists.

, , , and . Proc. Priv. Enhancing Technol., 2017 (1): 42-61 (2017)

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(Do Not) Track Me Sometimes: Users' Contextual Preferences for Web Tracking., , , , , and . Proc. Priv. Enhancing Technol., 2016 (2): 135-154 (2016)Secure authentication from facial attributeswith no privacy loss., , and . ACM Conference on Computer and Communications Security, page 1403-1406. ACM, (2013)Optimization-Guided Binary Diversification to Mislead Neural Networks for Malware Detection., , , , and . CoRR, (2019)The Ultimate Combo: Boosting Adversarial Example Transferability by Composing Data Augmentations., , , and . CoRR, (2023)Adversarial Training for Raw-Binary Malware Classifiers., , , , , and . USENIX Security Symposium, page 1163-1180. USENIX Association, (2023)How Risky Are Real Users' IFTTT Applets?, , , , , , and . SOUPS @ USENIX Security Symposium, page 505-529. USENIX Association, (2020)Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition., , , and . CCS, page 1528-1540. ACM, (2016)On the Suitability of Lp-Norms for Creating and Preventing Adversarial Examples., , and . CVPR Workshops, page 1605-1613. Computer Vision Foundation / IEEE Computer Society, (2018)A Field Study of Computer-Security Perceptions Using Anti-Virus Customer-Support Chats., , , , , , and . CHI, page 78. ACM, (2019)Adversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition., , , and . CoRR, (2018)