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A comprehensive study of frequency, interference, and training of multiple graphical passwords
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Conference on Human Factors in Computing Systems archive
Proceedings of the 27th international conference on Human factors in computing systems table of contents
Boston, MA, USA
SESSION: Security table of contents
Pages 889-898  
Year of Publication: 2009
ISBN:978-1-60558-246-7
Authors
Katherine M. Everitt  University of Washington, Seattle, WA, USA
Tanya Bragin  University of Washington, Seattle, WA, USA
James Fogarty  University of Washington, Seattle, WA, USA
Tadayoshi Kohno  University of Washington, Seattle, WA, USA
Sponsors
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Graphical password systems have received significant attention as one potential solution to the need for more usable authentication, but nearly all prior work makes the unrealistic assumption of studying a single password. This paper presents the first study of multiple graphical passwords to systematically examine frequency of access to a graphical password, interference resulting from interleaving access to multiple graphical passwords, and patterns of access while training multiple graphical passwords. We find that all of these factors significantly impact the ease of authenticating using multiple facial graphical passwords. For example, participants who accessed four different graphical passwords per week were ten times more likely to completely fail to authenticate than participants who accessed a single password once per week. Our results underscore the need for more realistic evaluations of the use of multiple graphical passwords, have a number of implications for the adoption of graphical password systems, and provide a new basis for comparing proposed graphical password systems.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Collaborative Colleagues:
Katherine M. Everitt: colleagues
Tanya Bragin: colleagues
James Fogarty: colleagues
Tadayoshi Kohno: colleagues