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Author Reber, Paul ♦ Bojinov, Hristo ♦ Lincoln, Patrick ♦ Boneh, Dan ♦ Sanchez, Daniel
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Language English
Abstract Cryptographic systems often rely on the secrecy of cryptographic keys given to users. Many schemes, however, cannot resist coercion attacks where the user is forcibly asked by an attacker to reveal the key. These attacks, known as rubber hose cryptanalysis, are often the easiest way to defeat cryptography. We present a defense against coercion attacks using the concept of implicit learning from cognitive psychology. Implicit learning refers to learning of patterns without any conscious knowledge of the learned pattern. We use a carefully crafted computer game to allow a user to implicitly learn a secret password without them having any explicit or conscious knowledge of the trained password. While the trained secret can be used for authentication, participants cannot be coerced into revealing it since they have no conscious knowledge of it. We performed a number of user studies using Amazon's Mechanical Turk to verify that participants can successfully re-authenticate over time and that they are unable to reconstruct or even robustly recognize the trained secret.
Description Affiliation: Northwestern University, Evanston, IL (Sanchez, Daniel; Reber, Paul) || Stanford University, Stanford, CA (Bojinov, Hristo; Boneh, Dan) || SRI International, Menlo Park, CA (Lincoln, Patrick)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2005-08-01
Publisher Place New York
Journal Communications of the ACM (CACM)
Volume Number 57
Issue Number 5
Page Count 9
Starting Page 110
Ending Page 118


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Source: ACM Digital Library