Bâce, M., Saad, A., Khamis, M. , Schneegass, S. and Bulling, A. (2022) PrivacyScout: Assessing Vulnerability to Shoulder Surfing on Mobile Devices. In: Privacy Enhancing Technologies Symposium 2022 (PETS 2022), Sydney, Australia, 11-15 Jul 2022, pp. 650-669. (doi: 10.56553/popets-2022-0090)
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Abstract
One approach to mitigate shoulder surfing attacks on mobile devices is to detect the presence of a bystander using the phone’s front-facing camera. However, a person’s face in the camera’s field of view does not always indicate an attack. To overcome this limitation, in a novel data collection study (N=16), we analysed the influence of three viewing angles and four distances on the success of shoulder surfing attacks. In contrast to prior works that mainly focused on user authentication, we investigated three common types of content susceptible to shoulder surfing: text, photos, and PIN authentications. We show that the vulnerability of text and photos depends on the observer’s location relative to the device, while PIN authentications are vulnerable independent of the observation location. We then present PrivacyScout – a novel method that predicts the shoulder-surfing risk based on visual features extracted from the observer’s face as captured by the front-facing camera. Finally, evaluations from our data collection study demonstrate our method’s feasibility to assess the risk of a shoulder surfing attack more accurately.
Item Type: | Conference Proceedings |
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Additional Information: | This project is partly funded by the Swiss National Science Foundation (SNSF) Early Postdoc. Mobility Fellowship (199991), the Deutsche Forschungsgemeinschaft (DFG) German Research Foundation (425869382), part of Priority Program SPP2199 Scalable Interaction Paradigms for Pervasive Computing Environments, EPSRC (EP/V008870/1) and the PETRAS National Centre of Excellence for IoT Systems Cybersecurity, which is also funded by the EPSRC (EP/S035362/1), and the the European Research Council (ERC; grant agreement 801708). |
Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Khamis, Dr Mohamed |
Authors: | Bâce, M., Saad, A., Khamis, M., Schneegass, S., and Bulling, A. |
College/School: | College of Science and Engineering > School of Computing Science |
ISSN: | 2299-0984 |
Copyright Holders: | Copyright © 2022 The Authors |
Publisher Policy: | Reproduced under a Creative Commons licence |
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