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Cited by 282

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A PIN-entry method resilient against shoulder surfing

Roth, Volker, Richter, Kai, Freidinger, Rene·2004·Proceedings of the 11th ACM conference on Computer and communications security

Abstract

Magnetic stripe cards are in common use for electronic payments and cash withdrawal. Reported incidents document that criminals easily pickpocket cards or skim them by swiping them through additional card readers. Personal identification numbers (PINs) are obtained by shoulder surfing, through the use of mirrors or concealed miniature cameras. Both elements, the PIN and the card, are generally sufficient to give the criminal full access to the victim's account. In this paper, we present alternative PIN entry methods to which we refer as cognitive trapdoor games. These methods make it significantly harder for a criminal to obtain PINs even if he fully observes the entire input and output of a PIN entry procedure. We also introduce the idea of probabilistic cognitive trapdoor games, which offer resilience to shoulder surfing even if the criminal records a PIN entry procedure with a camera. We studied the security as well as the usability of our methods, the results of which we also present in the paper.

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Metadata

Authors
Roth, V., Richter, K., & Freidinger, R.
Venue
Proceedings of the 11th ACM conference on Computer and communications security
Publisher
ACM
Published
2004-10-25
Pages
236-245
ISSN
1543-7221 (ISSN-L)
OpenAlex
W2078483465
Cited by
282
References
3
FWCI
5.41
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: A PIN-entry method resilient against shoulder surfing. https://app.cochat.ai/papers/10.1145/1030083.1030116
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Roth, V., Richter, K., & Freidinger, R. (2004). A PIN-entry method resilient against shoulder surfing. Proceedings of the 11th ACM conference on Computer and communications security. https://doi.org/10.1145/1030083.1030116

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.