“If it wasn’t secure, they would not use it in the movies” – Security perceptions and user acceptance of authentication technologies

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Abstract

Whereas the text password is still ubiquitous as authentication scheme, its shortcomings are well-acknowledged within the research community. A plurality of alternatives such as other knowledge-based, token-based or biometric authentication schemes have been developed. Although the usability of these schemes has been analyzed, the results concerning further user perceptions are complex and somewhat ambiguous. Further, most of these results stem from focus groups and surveys where the actual interaction with the systems was not tested. To shine light on this topic we conducted a laboratory study with 35 participants to compare and understand user perceptions of several biometric and non-biometric authentication schemes. We simulated the interaction with authentication schemes to protect our participants’ data and to avoid affecting influences of particular implementations. The results showed that the text password is still popular among the participants for reasons of familiarity and due to privacy aspects, namely because no personal information has to be provided. Fingerprint and iris recognition were well liked among the biometrics by many participants due to the perceived security of using a unique feature for authentication. However, the use of personal information also raised privacy concerns in others. This leads to the assumption that there might be two user groups preferring either passwords or biometrics. The assumption along with possible influencing variables such as authentication context or familiarity should be addressed in future research. The simulation of authentication schemes could further be improved by addressing realistic error rates to increase external validity of the study design.

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APA

Zimmermann, V., & Gerber, N. (2017). “If it wasn’t secure, they would not use it in the movies” – Security perceptions and user acceptance of authentication technologies. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10292 LNCS, pp. 265–283). Springer Verlag. https://doi.org/10.1007/978-3-319-58460-7_18

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