Skip to content

Bad Diagnosis: Analyzing Consent and Data Collection Practices of Mobile Medical Apps

Oct 2026 · 0 citations · 88 references
Computer Science

TL;DR

This first comprehensive analysis of consent and data collection practices across 10,000+ mHealth apps on the Google Play Store is presented, combining static analysis and privacy policy examination, with a rigorous operational definition to distinguish health apps from general wellness programs, and a measurement framework to detect misuse of permissions and violations of consent.

Abstract

Mobile health (mHealth) apps collect personally-identifiable information while handling uniquely sensitive medical data, ranging from mental health records to real-time glucose or heart rate monitoring -- yet they remain poorly understood at scale. We present the first comprehensive analysis of consent and data collection practices across 10,000+ mHealth apps on the Google Play Store, combining static analysis and privacy policy examination. We develop a rigorous operational definition to distinguish health apps from general wellness programs, and design a measurement framework to detect misuse of permissions and violations of consent. Our analyses reveal a troubling ecosystem: 69.15% of apps request at least one permission unrelated to their stated purpose, with over 52.9% apps requesting location data, and 50.5% and 34.9% apps requesting camera and microphone access, respectively -- despite no functional justification based on app descriptions and stated purposes. Most alarmingly, nearly 93% of apps fail to provide users with a consent form, directly violating regulatory requirements such as GDPR; and even among the remaining 7%, 1 in 14 demonstrate a gap between the obtained consent and collection of personally identifiable information. These findings demonstrate an urgent need for stronger enforcement of privacy regulations in mHealth apps. We plan to release our dataset and measurement tools for continued monitoring of this critical space.

View source

Similar papers

#computer vision Review Sep 2017

Agile Software Development Methods: Review and Analysis

This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.

P. Abrahamsson, O. Salo, Jussi Ronkainen et al. · 727 citations · ⚡54
#computer vision Jun 2008

The impact of agile practices on communication in software development

The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.

M. Pikkarainen, Jukka Haikara, O. Salo et al. · 401 citations · ⚡48
#machine learning Review Open access Oct 2014

Software development in startup companies: A systematic mapping study

The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.

Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al. · 394 citations · ⚡54

Related blog posts

Microsoft Research Blog Nov 5, 2026

Formally verifying agent-written full-stack web apps with PDD Kit

AI agents can generate code at a scale that’s hard for humans to review line-by-line. PDD Kit helps ensure the code is doing what the developer intended by creating formal specifications and testing against them. It converts developer intent into a Verus specification, proves AI-generated code meets it for every input, and uses tests where proofs aren't practical. Developers review intent, not every line. The post Formally verifying agent-written full-stack web apps with PDD Kit appeared first o…

Microsoft Research Blog Nov 3, 2026

Learn the rules: The hacker mindset

Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.

GPT-Lab Oct 30, 2026

Black October? The Week AI Changed the Cybersecurity Conversation

Critical flaws, compromised release channels and agentic AI threats landed within days of each other in mid-October 2026. Why “Black October” may say more about how well we now find weaknesses than about the attacks themselves. The post Black October? The Week AI Changed the Cybersecurity Conversation appeared first on GPT-Lab.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.