Privacy and Security of Women's Reproductive Health Apps in a Changing Legal Landscape
- URL: http://arxiv.org/abs/2404.05876v1
- Date: Mon, 8 Apr 2024 21:19:10 GMT
- Title: Privacy and Security of Women's Reproductive Health Apps in a Changing Legal Landscape
- Authors: Shalini Saini, Nitesh Saxena,
- Abstract summary: 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.
- Score: 1.7930036479971307
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: FemTech, a rising trend in mobile apps, empowers women to digitally manage their health and family planning. However, privacy and security vulnerabilities in period-tracking and fertility-monitoring apps present significant risks, such as unintended pregnancies and legal consequences. Our approach involves manual observations of privacy policies and app permissions, along with dynamic and static analysis using multiple evaluation frameworks. Our research reveals that many of these apps gather personally identifiable information (PII) and sensitive healthcare data. Furthermore, 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. Our research emphasizes the significance of tackling the privacy and security vulnerabilities present in period-tracking and fertility-monitoring mobile apps. By highlighting these crucial risks, we aim to initiate a vital discussion and advocate for increased accountability and transparency of digital tools for women's health. We encourage the industry to prioritize user privacy and security, ultimately promoting a safer and more secure environment for women's health management.
Related papers
- The Gradient of Health Data Privacy [15.417809900388262]
This paper introduces a novel "privacy gradient" approach to health data governance.
Our multidimensional concept considers factors such as data sensitivity, stakeholder relationships, purpose of use, and temporal aspects.
We demonstrate how this approach can address critical privacy challenges in diverse healthcare settings worldwide.
arXiv Detail & Related papers (2024-10-01T17:35:18Z) - Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives [48.48294460952039]
This survey offers comprehensive descriptions of the privacy, security, and fairness issues in federated learning.
We contend that there exists a trade-off between privacy and fairness and between security and sharing.
arXiv Detail & Related papers (2024-06-16T10:31:45Z) - 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-preserving Optics for Enhancing Protection in Face De-identification [60.110274007388135]
We propose a hardware-level face de-identification method to solve this vulnerability.
We also propose an anonymization framework that generates a new face using the privacy-preserving image, face heatmap, and a reference face image from a public dataset as input.
arXiv Detail & Related papers (2024-03-31T19:28:04Z) - Prioritizing Safeguarding Over Autonomy: Risks of LLM Agents for Science [65.77763092833348]
Intelligent agents powered by large language models (LLMs) have demonstrated substantial promise in autonomously conducting experiments and facilitating scientific discoveries across various disciplines.
While their capabilities are promising, these agents also introduce novel vulnerabilities that demand careful consideration for safety.
This paper conducts a thorough examination of vulnerabilities in LLM-based agents within scientific domains, shedding light on potential risks associated with their misuse and emphasizing the need for safety measures.
arXiv Detail & Related papers (2024-02-06T18:54:07Z) - 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) - White paper on cybersecurity in the healthcare sector. The HEIR solution [1.3717071154980571]
Patient data, including medical records and financial information, are at risk, potentially leading to identity theft and patient safety concerns.
The HEIR project offers a comprehensive cybersecurity approach, promoting security features from various regulatory frameworks.
These measures aim to enhance digital health security and protect sensitive patient data while facilitating secure data access and privacy-aware techniques.
arXiv Detail & Related papers (2023-10-16T07:27:57Z) - 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) - PrivHAR: Recognizing Human Actions From Privacy-preserving Lens [58.23806385216332]
We propose an optimizing framework to provide robust visual privacy protection along the human action recognition pipeline.
Our framework parameterizes the camera lens to successfully degrade the quality of the videos to inhibit privacy attributes and protect against adversarial attacks.
arXiv Detail & Related papers (2022-06-08T13:43:29Z) - 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) - COVI White Paper [67.04578448931741]
Contact tracing is an essential tool to change the course of the Covid-19 pandemic.
We present an overview of the rationale, design, ethical considerations and privacy strategy of COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.
arXiv Detail & Related papers (2020-05-18T07:40:49Z)
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.