Design and Evaluation of a Data-Driven Password Meter
Author(s): Blase Ur, Felicia Alfieri, Maung Aung, Lujo Bauer, Nicolas Christin, Jessica Colnago

Date: May 6 2017
Publication: Proceedings of the 2017 SIGCHI Conference on Human Factors in Computing Systems (CHI '17)
Publisher: ACM
Source 1: http://jessicacolnago.com/publications/Ur2017.pdf
Source 2: http://hdixon.io/pdfs/meterpaper.pdf

Abstract or Summary:
Despite their ubiquity, many password meters provide inaccurate strength estimates. Furthermore, they do not explain to users what is wrong with their password or how to improve it. We describe the development and evaluation of a data-driven password meter that provides accurate strength measurement and actionable, detailed feedback to users. This meter combines neural networks and numerous carefully combined heuristics to score passwords and generate data-driven text feedback about the userís password. We describe the meterís iterative development and final design. We detail the security and usability impact of the meterís design dimensions, examined through a 4,509-participant online study. Under the more common password-composition policy we tested, we found that the data-driven meter with detailed feedback led users to create more secure, and no less memorable, passwords than a meter with only a bar as a strength indicator.

PasswordResearch.com Note: Additional authors not listed above: Lorrie Faith Cranor, Henry Dixon, Pardis Emami Naeini, Hana Habib, Noah Johnson, William Melicher


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