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Measuring Real-World Accuracies and Biases in Modeling Password Guessability.

, , , , , , , , , and . USENIX Security Symposium, page 463-481. USENIX Association, (2015)

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Measuring Real-World Accuracies and Biases in Modeling Password Guessability., , , , , , , , , and . USENIX Security Symposium, page 463-481. USENIX Association, (2015)Design and Evaluation of a Data-Driven Password Meter., , , , , , , , , and 2 other author(s). CHI, page 3775-3786. ACM, (2017)A Spoonful of Sugar?: The Impact of Guidance and Feedback on Password-Creation Behavior., , , , , , , , , and . CHI, page 2903-2912. ACM, (2015)Designing Password Policies for Strength and Usability., , , , , , , , , and . ACM Trans. Inf. Syst. Secur., 18 (4): 13:1-13:34 (2016)Reasoning Analytically about Password-Cracking Software., , , , and . IEEE Symposium on Security and Privacy, page 380-397. IEEE, (2019)AutoTap: synthesizing and repairing trigger-action programs using LTL properties., , , , , and . ICSE, page 281-291. IEEE / ACM, (2019)Taking Data Out of Context to Hyper-Personalize Ads: Crowdworkers' Privacy Perceptions and Decisions to Disclose Private Information., , , , , and . CHI, page 1-13. ACM, (2020)Trigger-Action Programming in the Wild: An Analysis of 200, 000 IFTTT Recipes., , , , , , , and . CHI, page 3227-3231. ACM, (2016)Smart, useful, scary, creepy: perceptions of online behavioral advertising., , , , and . SOUPS, page 4. ACM, (2012)Fast, Lean, and Accurate: Modeling Password Guessability Using Neural Networks, , , , , , and . 25th USENIX Security Symposium (USENIX Security 16), page 175--191. Austin, TX, USENIX Association, (2016)