NDLI: Secrets, Lies, and Account Recovery: Lessons from the Use of Personal Knowledge Questions at Google

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Secrets, Lies, and Account Recovery: Lessons from the Use of Personal Knowledge Questions at Google

Content Provider	ACM Digital Library
Author	Caron, Ilan Bonneau, Joseph Bursztein, Elie Jackson, Rob Williamson, Mike
Abstract	We examine the first large real-world data set on personal knowledge question's security and memorability from their deployment at Google. Our analysis confirms that secret questions generally offer a security level that is far lower than user-chosen passwords. It turns out to be even lower than proxies such as the real distribution of surnames in the population would indicate. Surprisingly, we found that a significant cause of this insecurity is that users often don't answer truthfully. A user survey we conducted revealed that a significant fraction of users (37%) who admitted to providing fake answers did so in an attempt to make them "harder to guess" although on aggregate this behavior had the opposite effect as people "harden" their answers in the same and predictable way. On the usability side, we show that secret answers have surprisingly poor memorability despite the assumption that their reliability motivates their continued deployment. From millions of account recovery attempts we observed a significant fraction of users (e.g 40% of our English-speaking US users) were unable to recall their answers when needed. This is lower than the success rate of alternative recovery mechanisms such as SMS reset codes (over 80%). Comparing question strength and memorability reveals that the questions that are potentially the most secure (e.g what is your first phone number) are also the ones with the worst memorability. We conclude that it appears next to impossible to find secret questions that are both secure and memorable. Secret questions continue have some use when combined with other signals, but they should not be used alone and best practice should favor more reliable alternatives.
Starting Page	141
Ending Page	150
Page Count	10
File Format	PDF
ISBN	9781450334693
DOI	10.1145/2736277.2741691
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-05-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Account recovery Personal knowledge questions
Content Type	Text
Resource Type	Article

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