Lujo Bauer
where else to find them
no profile links on file
what they talk about
all 5 in the filters
where they keep going back
- SOUPS4 editions2020–2025
on a stage with
all 33 in the filters
who talks about the same things
- Lorrie Faith Cranor27 talks
- Blase Ur16 talks
- Elissa M. Redmiles5 talks
- Sunny Consolvo3 talks
- Emilee Rader8 talks
| title | conference | date |
|---|---|---|
| Misuse, Misreporting, Misinterpretation of Statistical Methods in Usable Privacy and... | SOUPS 2025 | 2025-08-10 |
Systematic review of 121 SOUPS papers over 20 years audits use of NHST and finds concrete failures: 23% use incorrect tests that ignore sample dependencies… | ||
| Adopting AI to Protect Industrial Control Systems: Assessing Challenges and... | SOUPS 2025 | 2025-08-10 |
Paper reports 18 interviews with ICS practitioners (13 operators, 5 vendors) and cites a survey where 10% using AI and 19% experimenting. It documents highly… | ||
| Towards Usable Security Analysis Tools for Trigger-Action Programming | SOUPS 2023 | 2023-08-06 |
Study evaluates usable security techniques for trigger-action programming by testing 447 Prolific users on writing declarative policies vs anti-patterns using… | ||
| Detecting iPhone Security Compromise in Simulated Stalking Scenarios: Strategies and... | SOUPS 2022 | 2022-08-07 |
Study runs 18 remote semi-structured interviews using four simulated iPhone compromise scenarios (location sharing, suspected spyware, iCloud compromise… | ||
| How Risky Are Real Users' IFTTT Applets? | SOUPS 2020 | 2020-08-10 |
Study collects and analyzes 732 applets from 28 participants using a browser extension and surveys to add context beyond public IFTTT listings. An automated… | ||
| Design and Evaluation of a Data-Driven Password Meter | CHI 2017 | 2017-05-06 |
A client-side password meter combines compact neural networks to estimate guessability with a regression-weighted set of 21 characteristics that generate… | ||
| Can Unicorns Help Users Compare Crypto Key Fingerprints? | CHI 2017 | 2017-05-06 |
A 661-participant online experiment compares eight fingerprint representations and two modes under a simulated 2^60 attacker and finds big differences: textual… | ||
| Fast, Lean, and Accurate: Modeling Password Guessability Using Neural Networks | USENIX Security '16 | 2016-08-10 |
Neural networks generate password guesses that beat PCFGs and Markov models, often improving success by roughly 50% at high guess counts (e.g., 15%→22% at… | ||
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