Neuroscience meets cryptography: Crypto primitives secure against rubber hose attacks

Hristo Bojinov, Daniel Sanchez, Paul Reber, Dan Boneh, Patrick Lincoln

Research output: Contribution to journalArticle

6 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)110-118
Number of pages9
JournalCommunications of the ACM
Volume57
Issue number5
DOIs
StatePublished - May 2014

ASJC Scopus subject areas

  • Computer Science(all)

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