An Empirical Evaluation of GDPR Compliance Violations in Android mHealth Apps
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Abstract (arXiv)
The purpose of the General Data Protection Regulation (GDPR) is to provide improved privacy protection. If an app controls personal data from users, it needs to be compliant with GDPR. However, GDPR lists general rules rather than exact step-by-step guidelines about how to develop an app that fulfills the requirements. Therefore, there may exist GDPR compliance violations in existing apps, which would pose severe privacy threats to app users. In this paper, we take mobile health applications (mHealth apps) as a peephole to examine the status quo of GDPR compliance in Android apps. We first propose an automated system, named mytool, to bridge the semantic gap between the general rules of GDPR and the app implementations by identifying the data practices declared in the app privacy policy and the data relevant behaviors in the app code. Then, based on mytool, we detect three kinds of GDPR compliance violations, including the incompleteness of privacy policy, the inconsistency of data collections, and the insecurity of data transmission. We perform an empirical evaluation of 796 mHealth apps. The results reveal that 189 (23.7%) of them do not provide complete privacy policies. Moreover, 59 apps collect sensitive data through different measures, but 46 (77.9%) of them contain at least one inconsistent collection behavior. Even worse, among the 59 apps, only 8 apps try to ensure the transmission security of collected data. However, all of them contain at least one encryption or SSL misuse. Our work exposes severe privacy issues to raise awareness of privacy protection for app users and developers.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Aug 2020.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 0; 2024: 1.
6 papers.
- 2022Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineeringcited by 20
- 2024Enhancing Legal Compliance and Regulation Analysis with Large Language Modelscited by 10
- 2021AI-enabled Automation for Completeness Checking of Privacy Policiescited by 7
- 2022A Fine-grained Chinese Software Privacy Policy Dataset for Sequence Labeling and Regulation Compliant Identificationcited by 3
- 2021Automating the GDPR Compliance Assessment for Cross-border Personal Data Transfers in Android Applicationscited by 0
- 2022NL2GDPR: Automatically Develop GDPR Compliant Android Application Features from Natural Languagecited by 0
- 2024Enhancing Legal Compliance and Regulation Analysis with Large Language Models26 Apr 2024
- 2022A Fine-grained Chinese Software Privacy Policy Dataset for Sequence Labeling and Regulation Compliant Identification4 Dec 2022
- 2022Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering12 Sep 2022
- 2022NL2GDPR: Automatically Develop GDPR Compliant Android Application Features from Natural Language29 Aug 2022
- 2021AI-enabled Automation for Completeness Checking of Privacy Policies10 Jun 2021
- 2021Automating the GDPR Compliance Assessment for Cross-border Personal Data Transfers in Android Applications12 Mar 2021