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Passive user identification using sequential analysis of proximity information in touchscreen usage patterns., , , and . ICMU, page 161-166. IEEE Computer Society, (2015)Fast, Lean, and Accurate: Modeling Password Guessability Using Neural Networks., , , , , , and . USENIX Security Symposium, page 175-191. USENIX Association, (2016)Towards Privacy-Aware Smart Buildings: Capturing, Communicating, and Enforcing Privacy Policies and Preferences., , , , , , , , , and 3 other author(s). ICDCS Workshops, page 193-198. IEEE Computer Society, (2017)Riding out DOMsday: Towards Detecting and Preventing DOM Cross-Site Scripting., , , , and . NDSS, The Internet Society, (2018)Fast, Lean, and Accurate: Modeling Password Guessability Using Neural Networks., , , , , and . USENIX ATC, USENIX Association, (2017)Sharing Personal Content Online: Exploring Channel Choice and Multi-Channel Behaviors., , , , , and . CHI, page 101-112. ACM, (2016)Automated Generation of Behavioral Signatures for Malicious Web Campaigns., , , , and . ISC (2), volume 15258 of Lecture Notes in Computer Science, page 226-245. Springer, (2024)Toward strong, usable access control for shared distributed data., , , , , , , and . FAST, page 89-103. USENIX, (2014)A Spoonful of Sugar?: The Impact of Guidance and Feedback on Password-Creation Behavior., , , , , , , , , and . CHI, page 2903-2912. ACM, (2015)Measuring Real-World Accuracies and Biases in Modeling Password Guessability., , , , , , , , , and . USENIX Security Symposium, page 463-481. USENIX Association, (2015)