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Expecting (Targeted Ads)? Network Analysis of User Health Data Leakage in Fertility Tracking Apps

arXiv Security Archived Jun 26, 2026 ✓ Full text saved

arXiv:2606.26276v1 Announce Type: new Abstract: While human factors in the privacy of fertility tracking apps -- health trackers that record user's menstrual or pregnancy data -- has been the subject of extensive study, little attention has been paid to the technical aspects of apps' data handling practices. We conduct a network-based measurement study of a corpus of 20 Android fertility tracking apps from the Google Play Store, focusing on how user data is shared with third party advertising se

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    Computer Science > Cryptography and Security [Submitted on 24 Jun 2026] Expecting (Targeted Ads)? Network Analysis of User Health Data Leakage in Fertility Tracking Apps Yeeun Jo, Shahanaasree Sivakumar, Mahnoor Jameel, Camille Cobb, Adam Bates, Brad Reaves While human factors in the privacy of fertility tracking apps -- health trackers that record user's menstrual or pregnancy data -- has been the subject of extensive study, little attention has been paid to the technical aspects of apps' data handling practices. We conduct a network-based measurement study of a corpus of 20 Android fertility tracking apps from the Google Play Store, focusing on how user data is shared with third party advertising services. After systematizing app features, we conduct a series of standardized user interactions across all apps in an environment that records TLS-stripped network traffic. In a subset of apps (n=5) we identify explicit leakage of user health data as well implicit leakage through highly targeted contextual advertising URL's. Equally importantly, we observe additional apps that use an ad-based monetization model without apparent leakage of user data, as well as several apps the interact only minimally with ad services. These findings provide technical grounding for widespread user concerns, but also underscore the importance of consumer choice in the privacy implications of app-based fertility tracking. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2606.26276 [cs.CR]   (or arXiv:2606.26276v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.26276 Focus to learn more Submission history From: Adam Bates [view email] [v1] Wed, 24 Jun 2026 18:17:04 UTC (44 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Jun 26, 2026
    Archived
    Jun 26, 2026
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