Conference item
Unwinding Ariadne's identity thread: Privacy risks with fitness trackers and online social networks
- 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 878.3KB, Terms of use)
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- Publisher copy:
- 10.1145/3137616.3137617
Authors
- Publisher:
- Association for Computing Machinery
- Host title:
- MPS '17 Proceedings of the 2017 on Multimedia Privacy and Security
- Journal:
- nternational Workshop on Multimedia Privacy and Security in conjunction with the 24th ACM Conference on Computer and Communication Security More from this journal
- Pages:
- 1-11
- Publication date:
- 2017-10-30
- Acceptance date:
- 2017-09-05
- DOI:
- ISBN:
- 9781450352062
- Keywords:
- Pubs id:
-
pubs:729219
- UUID:
-
uuid:ba5e5791-05ec-426d-9cfb-90d1121dc8a7
- Local pid:
-
pubs:729219
- Source identifiers:
-
729219
- Deposit date:
-
2017-09-18
- ARK identifier:
Terms of use
- Copyright holder:
- Association for Computing Machinery
- Copyright date:
- 2017
- Notes:
- Copyright © 2017 Association for Computing Machinery. This is the accepted manuscript version of the paper. The final version is available online from ACM at: https://doi.org/10.1145/3137616.3137617
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