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Use Your Illusion: secure authentication usable anywhere
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ACM International Conference Proceeding Series; Vol. 337 archive
Proceedings of the 4th symposium on Usable privacy and security table of contents
Pittsburgh, Pennsylvania
SESSION: Authentication II table of contents
Pages 35-45  
Year of Publication: 2008
ISBN:978-1-60558-276-4
Authors
Eiji Hayashi  CMU/CyLab Japan
Rachna Dhamija  Harvard University
Nicolas Christin  CMU/CyLab Japan
Adrian Perrig  CMU/CyLab
Publisher
ACM  New York, NY, USA
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ABSTRACT

In this paper, we propose and evaluate Use Your Illusion, a novel mechanism for user authentication that is secure and usable regardless of the size of the device on which it is used. Our system relies on the human ability to recognize a degraded version of a previously seen image. We illustrate how distorted images can be used to maintain the usability of graphical password schemes while making them more resilient to social engineering or observation attacks. Because it is difficult to mentally "revert" a degraded image, without knowledge of the original image, our scheme provides a strong line of defense against impostor access, while preserving the desirable memorability properties of graphical password schemes.

Using low-fidelity tests to aid in the design, we implement prototypes of Use Your Illusion as i) an Ajax-based web service and ii) on Nokia N70 cellular phones. We conduct a between-subjects usability study of the cellular phone prototype with a total of 99 participants in two experiments. We demonstrate that, regardless of their age or gender, users are very skilled at recognizing degraded versions of self-chosen images, even on small displays and after time periods of one month. Our results indicate that graphical passwords with distorted images can achieve equivalent error rates to those using traditional images, but only when the original image is known.


REFERENCES

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Collaborative Colleagues:
Eiji Hayashi: colleagues
Rachna Dhamija: colleagues
Nicolas Christin: colleagues
Adrian Perrig: colleagues