This map shows the geographic impact of Blase Ur's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Blase Ur with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Blase Ur more than expected).
This network shows the impact of papers produced by Blase Ur. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Blase Ur. The network helps show where Blase Ur may publish in the future.
Co-authorship network of co-authors of Blase Ur
This figure shows the co-authorship network connecting the top 25 collaborators of Blase Ur.
A scholar is included among the top collaborators of Blase Ur based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Blase Ur. Blase Ur is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Golla, Maximilian, et al.. (2021). "It's Stored, Hopefully, on an Encrypted Server'': Mitigating Users' Misconceptions About FIDO2 Biometric WebAuthn. USENIX Security Symposium. 91–108.4 indexed citations
5.
Serrano, Daniel, et al.. (2021). Pursuing Usable and Useful Data Downloads Under GDPR/CCPA Access Rights via Co-Design.. Symposium On Usable Privacy and Security. 217–242.8 indexed citations
6.
Mazurek, Michelle L., et al.. (2020). What Twitter Knows: Characterizing Ad Targeting Practices, User Perceptions, and Ad Explanations Through Users' Own Twitter Data.. USENIX Security Symposium. 145–162.16 indexed citations
Abu-Salma, Ruba, et al.. (2018). Exploring User Mental Models of End-to-End Encrypted Communication Tools. USENIX Security Symposium.13 indexed citations
9.
He, Weijia, et al.. (2018). Rethinking Access Control and Authentication for the Home Internet of Things (IoT). ePrints Soton (University of Southampton). 255–272.87 indexed citations
Segreti, Sean M., William Melicher, Saranga Komanduri, et al.. (2017). Diversify to survive: making passwords stronger with adaptive policies. Symposium On Usable Privacy and Security. 1–12.17 indexed citations
12.
Melicher, William, Blase Ur, Sean M. Segreti, et al.. (2017). Better Passwords through Science (and Neural Networks).. 42.4 indexed citations
13.
Tian, Yuan, et al.. (2017). SmartAuth: User-Centered Authorization for the Internet of Things. USENIX Security Symposium. 361–378.104 indexed citations
14.
Melicher, William, Blase Ur, Saranga Komanduri, et al.. (2016). Fast, Lean, and Accurate: Modeling Password Guessability Using Neural Networks. USENIX Security Symposium. 175–191.130 indexed citations
15.
Ur, Blase, Sean M. Segreti, Richard Shay, et al.. (2015). “I added '!' at the end to make it secure”: observing password creation in the lab. Symposium On Usable Privacy and Security. 123–140.66 indexed citations
16.
Ur, Blase, Sean M. Segreti, Lujo Bauer, et al.. (2015). Measuring real-world accuracies and biases in modeling password guessability. USENIX Security Symposium. 463–481.84 indexed citations
17.
Cranor, Lorrie Faith, et al.. (2014). Parents' and Teens' Perspectives on Privacy In a Technology-Filled World. Symposium On Usable Privacy and Security. 19–35.26 indexed citations
Ur, Blase, Patrick Gage Kelley, Saranga Komanduri, et al.. (2012). Helping users create better passwords. 37(6). 51–57.6 indexed citations
20.
Ur, Blase, Patrick Gage Kelley, Saranga Komanduri, et al.. (2012). How does your password measure up? the effect of strength meters on password creation. USENIX Security Symposium. 5–5.169 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.