Unveiling Privacy and Security Gaps in Female Health Apps
- URL: http://arxiv.org/abs/2502.02749v1
- Date: Tue, 04 Feb 2025 22:34:03 GMT
- Title: Unveiling Privacy and Security Gaps in Female Health Apps
- Authors: Muhammad Hassan, Mahnoor Jameel, Tian Wang, Masooda Bashir,
- Abstract summary: Investigation uncovers harmful permissions, extensive collection of sensitive personal and medical data, and the presence of numerous third-party tracking libraries.
Findings highlight a significant lack of privacy and security measures for FemTech apps, especially as women's reproductive rights face growing political challenges.
- Score: 4.387660388540319
- License:
- Abstract: Female Health Applications (FHA), a growing segment of FemTech, aim to provide affordable and accessible healthcare solutions for women globally. These applications gather and monitor health and reproductive data from millions of users. With ongoing debates on women's reproductive rights and privacy, it's crucial to assess how these apps protect users' privacy. In this paper, we undertake a security and data protection assessment of 45 popular FHAs. Our investigation uncovers harmful permissions, extensive collection of sensitive personal and medical data, and the presence of numerous third-party tracking libraries. Furthermore, our examination of their privacy policies reveals deviations from fundamental data privacy principles. These findings highlight a significant lack of privacy and security measures for FemTech apps, especially as women's reproductive rights face growing political challenges. The results and recommendations provide valuable insights for users, app developers, and policymakers, paving the way for better privacy and security in Female Health Applications.
Related papers
- Evaluating Privacy Measures in Healthcare Apps Predominantly Used by Older Adults [2.7039386580759666]
rapid growth has also heightened concerns about the privacy of their health information.
We evaluated 28 healthcare apps across multiple dimensions, including regulatory compliance, data handling practices, and privacy-focused usability.
Our analysis revealed significant gaps in compliance with privacy standards to such, only 25% of apps explicitly state compliance with HIPAA, and only 18% mention.
Surprisingly, 79% of these applications lack breach protocols, putting older adults at risk in the event of a data breach.
arXiv Detail & Related papers (2024-10-18T17:01:14Z) - Collection, usage and privacy of mobility data in the enterprise and public administrations [55.2480439325792]
Security measures such as anonymization are needed to protect individuals' privacy.
Within our study, we conducted expert interviews to gain insights into practices in the field.
We survey privacy-enhancing methods in use, which generally do not comply with state-of-the-art standards of differential privacy.
arXiv Detail & Related papers (2024-07-04T08:29:27Z) - A Qualitative Analysis Framework for mHealth Privacy Practices [0.0]
This paper introduces a novel framework for the qualitative evaluation of privacy practices in mHealth apps.
Our investigation encompasses an analysis of 152 leading mHealth apps on the Android platform.
Our findings indicate persistent issues with negligence and misuse of sensitive user information.
arXiv Detail & Related papers (2024-05-28T08:57:52Z) - Privacy and Security of Women's Reproductive Health Apps in a Changing Legal Landscape [1.7930036479971307]
Privacy and security vulnerabilities in period-tracking and fertility-monitoring apps present significant risks.
Our approach involves manual observations of privacy policies and app permissions, along with dynamic and static analysis.
Our analysis identifies that 61% of the code vulnerabilities found in the apps are classified under the top-ten Open Web Application Security Project (OWASP) vulnerabilities.
arXiv Detail & Related papers (2024-04-08T21:19:10Z) - What is in Your App? Uncovering Privacy Risks of Female Health Applications [4.387660388540319]
FemTech or Female Technology, is an expanding field dedicated to providing affordable and accessible healthcare solutions for women.
With the leading app exceeding 1 billion downloads, these applications are gaining widespread popularity.
This exploratory study delves into the privacy risks associated with seven popular applications.
arXiv Detail & Related papers (2023-10-23T01:46:29Z) - A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and
Applications [76.88662943995641]
Graph Neural Networks (GNNs) have gained significant attention owing to their ability to handle graph-structured data.
To address this issue, researchers have started to develop privacy-preserving GNNs.
Despite this progress, there is a lack of a comprehensive overview of the attacks and the techniques for preserving privacy in the graph domain.
arXiv Detail & Related papers (2023-08-31T00:31:08Z) - A Comprehensive Picture of Factors Affecting User Willingness to Use
Mobile Health Applications [62.60524178293434]
The aim of this paper is to investigate the factors that influence user acceptance of mHealth apps.
Users' digital literacy has the strongest impact on their willingness to use them, followed by their online habit of sharing personal information.
Users' demographic background, such as their country of residence, age, ethnicity, and education, has a significant moderating effect.
arXiv Detail & Related papers (2023-05-10T08:11:21Z) - Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment [100.1798289103163]
We present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP)
Key points and high-level contents of the article were originated from the discussions from "Differential Privacy (DP): Challenges Towards the Next Frontier"
This article aims to provide a reference point for the algorithmic and design decisions within the realm of privacy, highlighting important challenges and potential research directions.
arXiv Detail & Related papers (2023-04-14T05:29:18Z) - Privacy Explanations - A Means to End-User Trust [64.7066037969487]
We looked into how explainability might help to tackle this problem.
We created privacy explanations that aim to help to clarify to end users why and for what purposes specific data is required.
Our findings reveal that privacy explanations can be an important step towards increasing trust in software systems.
arXiv Detail & Related papers (2022-10-18T09:30:37Z) - On the Privacy of Mental Health Apps: An Empirical Investigation and its
Implications for Apps Development [14.113922276394588]
This paper reports an empirical study aimed at systematically identifying and understanding data privacy incorporated in mental health apps.
We analyzed 27 top-ranked mental health apps from Google Play Store.
The findings reveal important data privacy issues such as unnecessary permissions, insecure cryptography implementations, and leaks of personal data and credentials in logs and web requests.
arXiv Detail & Related papers (2022-01-22T09:23:56Z) - Beyond privacy regulations: an ethical approach to data usage in
transportation [64.86110095869176]
We describe how Federated Machine Learning can be applied to the transportation sector.
We see Federated Learning as a method that enables us to process privacy-sensitive data, while respecting customer's privacy.
arXiv Detail & Related papers (2020-04-01T15:10:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.