On the Privacy of Mental Health Apps: An Empirical Investigation and its Implications for Apps Development

TL;DR

This study empirically examines privacy issues in top mental health apps, revealing vulnerabilities like unnecessary permissions and data leaks, and offers recommendations for improving privacy protections in mHealth applications.

Contribution

It provides a comprehensive privacy analysis of 27 mental health apps, mapping threats to the LINDDUN taxonomy and highlighting specific vulnerabilities and risks.

Findings

Unnecessary permissions and insecure cryptography found

Leaks of personal data and credentials in logs and web requests

High risk of user profiling due to app design flaws

Abstract

An increasing number of mental health services are offered through mobile systems, a paradigm called mHealth. Although there is an unprecedented growth in the adoption of mHealth systems, partly due to the COVID-19 pandemic, concerns about data privacy risks due to security breaches are also increasing. Whilst some studies have analyzed mHealth apps from different angles, including security, there is relatively little evidence for data privacy issues that may exist in mHealth apps used for mental health services, whose recipients can be particularly vulnerable. 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. Our methodology enabled us to perform an in-depth privacy analysis of the apps, covering static and dynamic analysis, data sharing behaviour, server-side tests, privacy impact assessment requests, and privacy policy evaluation. Furthermore, we mapped the findings to the LINDDUN threat taxonomy, describing how threats manifest on the studied apps. 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. There is also a high risk of user profiling as the apps' development do not provide foolproof mechanisms against linkability, detectability and identifiability. Data sharing among third parties and advertisers in the current apps' ecosystem aggravates this situation. Based on the empirical findings of this study, we provide recommendations to be considered by different stakeholders of mHealth apps in general and apps developers in particular. [...]