Security and Privacy Analysis of Mobile Health Applications: The Alarming State of Practice
Open accessAbstract
Recent advances in hardware and telecommunications have enabled the development of low cost mobile devices equipped with a variety of sensors. As a result, new functionalities, empowered by emerging mobile platforms, allow millions of applications to take advantage of vast amounts of data. Following this trend, mobile health applications collect users health-related information to help them better comprehend their health status and to promote their overall wellbeing. Nevertheless, health-related information is by nature and by law deemed sensitive and, therefore, its adequate protection is of substantial importance. In this paper we provide an in-depth security and privacy analysis of some of the most popular freeware mobile health applications. We have performed both static and dynamic analysis of selected mobile health applications, along with tailored testing of each application's functionalities. Long term analyses of the life cycle of the reviewed apps and our general data protection regulation compliance auditing procedure are unique features of the present paper. Our findings reveal that the majority of the analyzed applications do not follow well-known practices and guidelines, not even legal restrictions imposed by contemporary data protection regulations, thus jeopardizing the privacy of millions of users.
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Others report concerns from the point of view of security, privacy, and ethical implications of mental health apps.
“others report concerns from the point of view of security, privacy, and ethical implications [14,15].”
From A conversational artificial intelligence agent for a mental health care app: Evaluation study of its participatory design (2021) · cites this paper as [14]
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Security of Ubiquitous Computing Systems
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The Internet of Things in Elderly Healthcare Applications: A Systematic Review and Future Directions
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- Authors
- Papageorgiou, A., Strigkos, M., Politou, E., Alepis, E., Solanas, A., & Patsakis, C.
- Affiliations
- University of Piraeus (GR)
- Venue
- IEEE Access
- Published
- 2018
- Volume
- 6
- Pages
- 9390-9403
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2787835938
- Cited by
- 236
- References
- 28
- Citations by year
- 2026: 11 · 2025: 27 · 2024: 25 · 2023: 33 · 2022: 34 · 2021: 30 · 2020: 35 · 2019: 30 · 2018: 11
- FWCI
- 40.47
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- Rovira & Virgili repository (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Commission under the Horizon 2020 Programme (H2020)
- OPERANDO project (653704)
- CRYPTACUS COST action (COST Action IC1403)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- mobile health apps, privacy, security analysis, health data protection, data protection regulation, static and dynamic analysis, application life cycle, compliance audit, sensitive user data
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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