Aktypi, Angeliki, Nurse, Jason R. C., Goldsmith, Michael (2017) Unwinding Ariadne's Identity Thread: Privacy Risks with Fitness Trackers and Online Social Networks. In: Proceedings of the 2017 International Workshop on Multimedia Privacy and Security. CCS Computer and Communications Security . ACM, New York, USA ISBN 978-1-4503-5206-2. (doi:10.1145/3137616.3137617) (KAR id:67470)
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Official URL: http://dx.doi.org/10.1145/3137616.3137617 |
Abstract
The recent expansion of Internet of Things (IoT) and the growing trends towards a healthier lifestyle, have been followed by a proliferation in the use of fitness-trackers in our daily life. These wearable IoT devices combined with the extensive use by individuals of Online Social Networks (OSNs) have raised many security and privacy concerns. Individuals enrich the content of their online posts with their physical performance and attendance at sporting events, without considering the plausible risks that this may result in. This paper aims to examine the potential exposure of users' identity that is caused by information that they share online and personal data that are stored by their fitness-trackers. We approach the privacy concerns that arise by building an interactive tool. This tool models online information shared by individuals and elaborates on how they might be exposed to the unwanted leakage of further personal data. The tool also illustrates the privacy risks that arise from information that people expose, which could be exploited by malicious parties such as fraudsters, stalkers and other online and offline criminals. To understand the level of users' awareness concerning their identity exposure when engaging with such devices and online services, we also have conducted a qualitative analysis and present our findings here.
Item Type: | Conference or workshop item (Proceeding) |
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DOI/Identification number: | 10.1145/3137616.3137617 |
Uncontrolled keywords: | Privacy; Internet of Things; Wearables; Fitness Trackers; Online Social Networks; Identity Exposure |
Subjects: |
Q Science T Technology |
Divisions: |
Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts |
Depositing User: | Jason Nurse |
Date Deposited: | 03 Jul 2018 17:00 UTC |
Last Modified: | 05 Nov 2024 11:07 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/67470 (The current URI for this page, for reference purposes) |
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