A threat taxonomy for mHealth privacy
Abstract
Networked mobile devices have great potential to enable individuals (and their physicians) to better monitor their health and to manage medical conditions. In this paper, we examine the privacy-related threats to these so-called mHealth technologies. We develop a taxonomy of the privacy-related threats, and discuss some of the technologies that could support privacy-sensitive mHealth systems. We conclude with a brief summary of research challenges.
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Privacy of information within mHealth apps may be compromised by malicious hacking, commercial data-sharing practices, or devices being stolen.
“However, there is also growing awareness and concern about the privacy of information within mHealth apps, which may be compromised by malicious hacking, through commercial data-sharing practices [6] [9], or devices being stolen [10].”
From Security and Privacy of mHealth Applications: A Scoping Review (2020) · cites this paper as [10]
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References 35 · Cited by 101
- Authors
- Kotz, D.
- Affiliations
- Dartmouth College (US)
- Corresponding author
- Kotz, D
- ORCID
- Kotz, D
- Venue
- 2011 Third International Conference on Communication Systems and Networks (COMSNETS 2011)
- Publisher
- IEEE
- Published
- 2011-01
- Pages
- 1-6
- OpenAlex
- W2133142765
- Cited by
- 101
- References
- 35
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 1 · 2022: 4 · 2021: 8 · 2020: 8 · 2019: 5 · 2018: 8 · 2017: 9 · 2016: 8 · 2015: 15 · 2014: 12 · 2013: 8 · 2012: 7
- FWCI
- 8.01
- Type
- conference paper
- Language
- en
- Open access
- closed
- Funders
- National Science Foundation
- Intel Corporation
- Dartmouth College
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Keywords
- mobile health, privacy risks, threat taxonomy, privacy-preserving techniques, health data privacy
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