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REVIEW 3 major objections 6 minor 104 references

Unveiling Privacy and Security Gaps in Female Health Apps

T0 review · 3 major / 6 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read Popular female health apps in the U.S. carry systemic privacy and security gaps, from over-broad permissions and embedded trackers to unreadable, non-specific privacy policies.

desk verdict A solid, re-implementable audit of 45 female health apps; the core finding holds, but the paper overstates what static analysis proves, needs numerical cleanup, and would benefit from releasing per-app data. read the letter →

arxiv 2502.02749 v1 pith:45VO2WZ2 submitted 2025-02-04 cs.HC cs.CR

Muhammad Hassan , Mahnoor Jameel , Tian Wang , Masooda Bashir This is my paper · ORCID
classification cs.HCcs.CR
keywords femalehealthapplicationsFemTechprivacymobileapppermissionsthird-partytrackerspoliciesdatacollectionreproductive
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to show that popular female health apps in the U.S. are failing, as a class, to protect the sensitive data they collect. It audits 45 free, popular Android apps with more than 587 million combined downloads, covering permissions, third-party trackers, user-facing data requests, and privacy-policy text. The authors find that nearly every app embeds tracking software, that most apps request permissions unrelated to their stated function, and that policies describing data sharing are long, vague, and written at a college reading level. The stakes the paper points to are concrete: in a post-Roe legal environment, menstrual, pregnancy, and sexual-activity data collected by these apps could be used in ways users cannot foresee or control. If the findings hold, the female health app market is currently offering convenience without the privacy infrastructure that health data demands.

What carries the argument

The argument is carried by a four-part audit built for this study. Static reverse-engineering of app packages yields the permission list, classified by Android's official permission categories plus an 'unknown' category for permissions Android does not document. A tracker-detection scan identifies embedded third-party libraries and groups them by function such as analytics, advertising, and profiling. Manual simulated use of each app, with reviewers documenting each screen, captures the categories of personal, reproductive, physical, and mental health data actually requested from a user. Finally, privacy policies are coded against fair information practice principles (transparency, minimization, user rights, and the like) and scored for readability with Flesch-Kincaid metrics. The combination matters because each layer independently exposes a gap, and together they show that the apps' technical capabilities, data requests, and disclosed policies are out of alignment.

What would settle it

Run the same permission, tracker, and policy audit on a random sample of 45 female health apps drawn from a different market, for example smaller or non-U.S. apps, or combine the static scan with a network proxy that records actual data egress while test accounts enter synthetic health data; if tracker prevalence and third-party sharing rates fall far below the reported levels, or if trackers rarely transmit health-derived data, the systemic-gap claim would be weakened.

Watch

Extended reading notes

Core claim

The central claim is that female health apps in the U.S. have systemic privacy and security gaps, not isolated incidents. Across the 45-app dataset, the paper reports: 95% of apps contain third-party trackers, with Google and Facebook trackers accounting for 64% of all tracker occurrences; the average app requests 21 permissions versus a typical Android baseline of about five, and 43% of requested permissions cannot be mapped to the app's stated core functionality; 85% of privacy policies acknowledge third-party data sharing, but most do not say which parties receive data or for what purpose; and average privacy-policy readability is far below the recommended benchmark, requiring college-level comprehension. The paper presents these patterns as the result of a sector that collects sensitive reproductive, physical, mental, and demographic data without adhering to data-minimization or transparency principles, and it argues the consequences are especially severe now that abortion-related data can carry legal risk.

Load-bearing premise

The load-bearing premise is that the 45 popular, free, U.S.-listed Android apps chosen through simulated Play Store searches stand in for the female health app ecosystem as a whole, so aggregate figures like 95% tracker prevalence describe the sector rather than just this sample.

Editorial extensions

If this is right

  • Users cannot realistically make informed consent decisions when many policies are missing, generic, or written at college level.
  • App-store permission screens and data-safety labels understate the risk, since 43% of permissions are not tied to the app's core functionality and 'unknown' permissions are widespread.
  • Because trackers from Google and Facebook dominate, a small set of companies receives a large share of the highly sensitive data these apps handle.
  • Regulators would need to treat female health apps like health entities rather than ordinary consumer software for the gaps to close, e.g., by extending health-privacy rules and enforcing data minimization.
  • Developers can reduce risk by mapping each permission and tracker to a core feature and deleting the rest, a recommendation the paper directly makes.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A direct traffic-measurement follow-up would tell whether trackers merely have access or actively transmit health-derived identifiers; the paper's static evidence establishes potential, not proven egress.
  • The large 'unknown permission' bucket is itself a finding about Android's transparency; a public registry for custom permissions would let users and auditors evaluate apps without reverse engineering.
  • Since the same audit could be repeated on a different market sample, one testable extension is whether apps sold outside the U.S., or smaller apps, show the same or different rates of tracker embedding and policy opacity.
  • Over the next few years, repeating this audit could serve as a benchmark for whether post-Roe regulatory or press pressure actually changes app behavior.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper reports a security and privacy assessment of 45 popular free female health apps (FHAs) from the U.S. Google Play Store, using static APK analysis (Androguard) to catalog permissions and Exodus to detect embedded third-party trackers, manual in-app exploration to document user data requests, and a qualitative/readability analysis of privacy policies against FIPPs and Flesch-Kincaid benchmarks. The headline findings are that FHAs request numerous permissions (including some unrelated to core functions), exhibit high third-party tracker prevalence (95% of apps), collect extensive sensitive personal and reproductive health data, and have privacy policies that are often missing, vague, or difficult to read. The paper interprets these findings as evidence of significant privacy and security gaps in the FHA ecosystem and offers recommendations for users, developers, and policymakers.

Significance. If the descriptive claims hold, this is a useful contribution to the FemTech privacy literature: the dataset is larger than many prior studies (45 apps vs. 11--30 in related work), the methodology combines technical static analysis with manual interaction and privacy-policy coding, and the evaluation uses external benchmarks (Google permission classifications, FIPPs, Flesch-Kincaid thresholds, and earlier published findings). The main empirical pattern--weak privacy protections and opaque data practices in popular female health apps--is consistent with prior work and is policy-relevant in the post-Dobbs U.S. context. However, the paper's central 'data sharing' claim overreaches what static analysis can establish, and several reported numerical aggregates are internally inconsistent, so the magnitudes of the headline findings need correction and verification before the results can be fully trusted.

major comments (3)
  1. [§4.2, §4.2.3, §5.3, Abstract, §8] The paper repeatedly states that trackers 'collect and transmit user data' (Section 4.2) and that Google/Facebook trackers 'collect vast amounts of sensitive user information' (Section 4.2.2), and the conclusion says user data is 'being shared with them.' However, Section 6 explicitly concedes that the static analysis shows what apps 'can potentially access, but not necessarily what they actively collect or share during actual usage.' Exodus identifies embedded SDKs, not network traffic. The abstract's 'extensive collection' and the conclusion's 'data being shared' therefore overstate the evidence. This is load-bearing for the paper's central claim of data sharing. I recommend rephrasing these statements to 'embedded trackers that can potentially collect and transmit user data' unless dynamic traffic analysis (e.g., interception of real network flows) is performed, and ensuring the abstract and conclusion carry the same caveat as Section 6.
  2. [§4.1.1, Table 2, §4.2] There are internal numerical inconsistencies that block verification of the reported magnitudes. (a) Section 4.1.1 says '28 unique Normal permissions identified,' while Table 2 lists 30 Normal permissions; Section 4.1.2 says 118 unique permissions total, which matches the table's 30+27+58+3=118 but not the text's 28. (b) Section 4.2 states there are 252 tracker occurrences across 45 apps with 'an average of 7 trackers per FHA,' but 252/45 = 5.6; even restricting to the 43 apps with trackers gives 252/43 ≈ 5.9. These discrepancies need to be reconciled or corrected, and the per-app permission and tracker counts should be reported in a table or appendix so that the averages and percentages can be independently checked.
  3. [§4.3.1, §4.3.2, §5.4] The privacy-policy statistics are inconsistent across sections. Section 4.3.1 reports that 15% of apps have no privacy policy on the Google Play Store, but Section 5.4 says '20% of FHAs lacked an accessible privacy policy on the Google Play Store.' Similarly, Section 4.3.2 says 60% of FHAs use generic or non-specific policies, while Section 5.4 says 45% of apps only provided generic or developer-level policies. The definitions of 'generic' and 'non-specific' may differ, but the paper does not reconcile these numbers, leaving the reader unsure which figures are correct. Please provide a single consistent set of counts, with the coding scheme from Appendix A applied transparently.
minor comments (6)
  1. [Table 4] Table 4 has row-numbering problems: #44 appears twice (IMC Women's Health and Bloomth), #45 is missing, and the rows for #23--#25 are formatted inconsistently. This makes it difficult to map app IDs to specific apps and to verify the dataset composition.
  2. [§3.4 and §4.4] The phrase 'dynamic interaction analysis' is used for what is essentially manual UI exploration and logging of user-entered data requests. This is not dynamic analysis in the network-traffic sense used in the mobile security literature. Please rename this to 'manual interaction analysis' or 'user data request analysis' to avoid confusion with dynamic taint tracking or traffic interception.
  3. [§4.3.3] The Flesch-Kincaid benchmark is stated as 'should score at least 60 on the Flesch Reading Ease scale,' but the source of this 'best practices' threshold is not cited. Please provide a reference for this criterion.
  4. [§Appendix A] The appendix uses both 'FIPPs' and 'FIPPS' spellings; please standardize. Also, Table 6 contains a typo ('Harmone' should be 'Hormone') and Figure 8's subcaptions are poorly labeled.
  5. [Throughout] There are numerous typographical errors and informal phrases that need copyediting, e.g., 'FetmTech' in Section 1, 'appls' in Section 3.1, 'core tenant' (should be 'tenet') in Section 5.1, and inconsistent comma/semicolon usage throughout.
  6. [§6] The Limitations section does not mention the internal numerical inconsistencies or the lack of released per-app data. Please add a sentence acknowledging that the aggregate counts have been corrected and that raw data are (or are not) available for independent verification.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the study is an external-benchmark empirical analysis; self-citations are peripheral and no result is defined in terms of its own conclusion.

full rationale

The paper's claims are derived from independent measurements: app selection from Google Play Store search results, permission classification using Google's official permission categories, tracker detection with Exodus Privacy, privacy-policy scoring against FIPPs and Flesch-Kincaid readability thresholds, and manually documented dynamic interactions. None of these inputs is defined in terms of the paper's conclusions, and no fitted parameter is later relabeled as a prediction. The abstract's summary statements (e.g., 95% tracker prevalence, 85% data-sharing policies) are aggregates of coded observations, not assumptions. The only author-overlapping citations, [37] and [45], support general background claims about privacy-policy complexity and user trust; they are not load-bearing for the central measurement results, which are also corroborated by non-author references and external tools. The skeptical concern that static tracker detection does not prove active data transmission is an evidentiary limitation, not a circular step: Section 6 explicitly concedes that static analysis shows what apps 'can potentially access, but not necessarily what they actively collect or share,' so the paper does not define transmission into existence. Internal inconsistencies (e.g., Normal permissions reported as both 28 and 30) affect reproducibility and correctness but do not make any claim equivalent to its input by construction. The study is therefore self-contained against external benchmarks and exhibits no significant circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new entities, free parameters, or fitted constants. Its conclusions rest on three domain assumptions: that static detection tools capture relevant risk-relevant capabilities, that short manual exploration captures typical data collection, and that FIPPs and readability metrics are appropriate evaluative standards.

assumptions (3)
  • domain assumption Static tool outputs from Androguard and Exodus Privacy accurately identify requested permissions and embedded third-party trackers, and these are meaningful indicators of privacy risk.
    Sections 3.2 and 3.3 rely on these tools without ground-truth validation. The paper acknowledges in Section 6 that static analysis shows potential, not actual, data flows.
  • domain assumption Manual exploration of each app for 10 to 15 minutes captures the data collection a typical free user would encounter.
    Section 3.4 relies on three reviewers manually exploring each app until features are exhausted; behind-paywall or long-term-use data requests may be missed.
  • domain assumption FIPPs principles and Flesch-Kincaid readability scores are valid external benchmarks for judging privacy policy quality.
    Section 3.5 selects FIPPs as the framework and 60 Flesch Reading Ease as a readability threshold without validating these choices against actual user comprehension in this context.

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Pith. "Pith review of Unveiling Privacy and Security Gaps in Female Health Apps." pith.science (2026). https://pith.science/paper/45VO2WZ2

@misc{pith2026250202749,
  author       = {Pith},
  title        = {Pith review of: Unveiling Privacy and Security Gaps in Female Health Apps},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/45VO2WZ2}},
  note         = {Machine review of arXiv:2502.02749}
}
read the original abstract

Female Health Applications (FHA), a growing segment of FemTech, aim to provide affordable and accessible healthcare solutions for women globally. These applications gather and monitor health and reproductive data from millions of users. With ongoing debates on women's reproductive rights and privacy, it's crucial to assess how these apps protect users' privacy. In this paper, we undertake a security and data protection assessment of 45 popular FHAs. Our investigation uncovers harmful permissions, extensive collection of sensitive personal and medical data, and the presence of numerous third-party tracking libraries. Furthermore, our examination of their privacy policies reveals deviations from fundamental data privacy principles. These findings highlight a significant lack of privacy and security measures for FemTech apps, especially as women's reproductive rights face growing political challenges. The results and recommendations provide valuable insights for users, app developers, and policymakers, paving the way for better privacy and security in Female Health Applications.

Figures

Figures reproduced from arXiv: 2502.02749 by the authors.

Figure 5
Figure 5. Frequency of Trackers in each Category Our analysis reveals the widespread presence of third-party trackers within FHAs, with a total of 252 occurrences across the dataset and 51 unique trackers identified. Trackers, which collect and transmit user data, are embedded in 95% (43 out of 45) of the FHAs in our sample. The number of trackers varies, ranging from 1 to 15 per app, with an average of 7 trackers per FHA. Th… view at source ↗
Figure 8
Figure 8. Permissions and Trackers Across all FHAs (a) Permissions Across FHAs Manuscript submitted to ACM (b) Trackers Across FHAs [PITH_FULL_IMAGE:figures/full_fig_p031_8.png] view at source ↗

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Reference graph

Works this paper leans on

104 extracted references · 79 canonical work pages

  1. [1]

    Nicole Acevedo. 2019. Nearly 900 women’s health clinics have lost federal funding over gag rule. https://www.nbcnews.com/news/latino/nearly- 900-women-s-health-clinics-have-lost-federal-funding-n1069591

    2019

  2. [2]

    Rajindra Adhikari, Deborah Richards, and Karen Scott. 2014. Security and privacy issues related to the use of mobile health apps. In 25th Australasian Conference on Information Systems. ACIS, Australasian Conference on Information Systems. https://api.semanticscholar.org/CorpusID:108915339

    2014

  3. [3]

    Esma Aïmeur, Oluwa Lawani, and Kimiz Dalkir. 2016. When changing the look of privacy policies affects user trust: An experimental study. Computers in Human Behavior 58 (2016), 368–379

    2016

  4. [4]

    Ke’ala Akau. 2021. Do You Know What Femtech Is? I Didn’t — medicine.yale.edu. https://medicine.yale.edu/news-article/do-you-know-what- femtech-is-i-didnt/

    2021

  5. [5]

    Olivia Aldridge. 2024. Interstate travel becomes a target for the anti-abortion movement with Texas filing. https://www.npr.org/2024/05/17/ 1252218618/interstate-travel-becomes-a-target-for-the-anti-abortion-movement-with-texas-fil 22 Hassan et al. Manuscript submitted to ACM

    2024

  6. [6]

    Najd Alfawzan, Markus Christen, Giovanni Spitale, and Nikola Biller-Andorno. 2022. Privacy, Data Sharing, and Data Security Policies of Women’s mHealth Apps: Scoping Review and Content Analysis. JMIR Mhealth Uhealth 10, 5 (6 May 2022), e33735. https://doi.org/10.2196/33735

    work page doi:10.2196/33735 2022

  7. [7]

    Najd Alfawzan, Markus Christen, Giovanni Spitale, and Nikola Biller-Andorno. 2022. Privacy, Data Sharing, and Data Security Policies of Women’s mHealth Apps: Scoping Review and Content Analysis. JMIR mHealth and uHealth 10, 5 (2022), e33735

    2022

  8. [8]

    Teresa Almeida, Laura Shipp, Maryam Mehrnezhad, and Ehsan Toreini. 2022. Bodies Like Yours: Enquiring Data Privacy in FemTech. In Adjunct Proceedings of the 2022 Nordic Human-Computer Interaction Conference. ACM, 1–5

    2022

Show all 104 references
  1. [9]

    Android. [n. d.]. permission Android Developers. https://developer.android.com/guide/topics/manifest/permission-element#pgroup

  2. [10]

    Your period starts in two days

    Sini-Marja Ant-Wuorinen, Maria Knaapi, Heli Koskela, Emma Lindberg, Anni Lintula, and Linda Palenius. 2023. “Your period starts in two days” Risks of period-tracking post Roe v. Wade. University of Helsinki (2023)

    2023

  3. [11]

    Zinzi D Bailey, Nancy Krieger, Madina Agénor, Jasmine Graves, Natalia Linos, and Mary T Bassett. 2017. Structural racism and health inequities in the USA: evidence and interventions. The lancet 389, 10077 (2017), 1453–1463

    2017

  4. [12]

    Rebecca Balebako, Abigail Marsh, Jialiu Lin, Jason I Hong, and Lorrie Faith Cranor. 2014. The privacy and security behaviors of smartphone app developers. NDSS (2014)

    2014

  5. [13]

    Elina Berglund Scherwitzl, Angelica Lindén Hirschberg, and Raoul Scherwitzl. 2015. Identification and prediction of the fertile window using NaturalCycles. The European Journal of Contraception & Reproductive Health Care 20, 5 (2015), 403–408

    2015

  6. [14]

    BMJ. 2020. Twenty five years after the Beijing Declaration we need to reaffirm that women’s rights are human rights. https://blogs.bmj.com/bmj/ 2020/03/08/twenty-five-years-after-the-beijing-declaration-we-need-to-reaffirm-that-womens-rights-are-human-rights/

    2020

  7. [15]

    Priyom Bose. 2023. The Femtech Revolution: How Technology is Empowering Women’s Health and Wellness. https://www.news-medical.net/ health/The-FemTech-Revolution-how-technology-is-empowering-Womens-health-and-wellness.aspx

    2023

  8. [16]

    Ann Cavoukian et al. 2021. Privacy by design: The seven foundational principles. IAPP Resource Center, https://iapp. org/resources/article/privacy-by- design-the-7-foundational-principles (2021)

    2021

  9. [17]

    Constitution Center. 2022. Dobbs v. Jackson Women’s Health Organization (2022). https://constitutioncenter.org/the-constitution/supreme-court- case-library/dobbs-v-jackson-womens-health-organization

    2022

  10. [18]

    Amita Goyal Chin, Mark A Harris, and Robert Brookshire. 2018. A bidirectional perspective of trust and risk in determining factors that influence mobile app installation. International Journal of Information Management 39 (2018), 49–59

    2018

  11. [19]

    Geumhwan Cho, Junsung Cho, Youngbae Song, and Hyoungshick Kim. 2015. An empirical study of click fraud in mobile advertising networks. In 2015 10th International Conference on Availability, Reliability and Security. IEEE, 382–388

    2015

  12. [20]

    Danielle Keats Citron. 2018. A poor mother’s right to privacy: A review. BUL Rev. 98 (2018), 1139

    2018

  13. [21]

    Kristin Cohen. 2022. Location, health, and other sensitive information: FTC committed to fully enforcing the law against illegal use and sharing of highly sensitive data. https://www.ftc.gov/business-guidance/blog/2022/07/location-health-and-other-sensitive-information-ftc-com...

    2022

  14. [22]

    Wylie Communication. 2021. What’s the latest U.S. literacy rate? https://www.wyliecomm.com/2021/08/whats-the-latest-u-s-literacy-rate/

    2021

  15. [23]

    Kenan Degirmenci. 2020. Mobile users’ information privacy concerns and the role of app permission requests. International Journal of Information Management 50 (2020), 261–272

    2020

  16. [24]

    Jim Edwards. [n. d.]. One of Israel’s most important tech IPO candidates is accused of using ’deceptive’ malware — businessinsider.com. https: //www.businessinsider.com/ironsource-denies-its-for-malware-2015-3?op=1

    2015

  17. [25]

    Vittoria Elliott. 2022. The fall of “Roe” would put big tech in a bind. https://www.wired.com/story/big-tech-roe-abortion/

    2022

  18. [26]

    William Enck. 2011. Defending users against smartphone apps: Techniques and future directions. In Information Systems Security: 7th International Conference, ICISS 2011, Kolkata, India, December 15-19, 2011, Procedings 7. Springer, 49–70

    2011

  19. [27]

    Jacob Erickson, Jewel Y Yuzon, and Tamara Bonaci. 2022. What You Do Not Expect When You Are Expecting: Privacy Analysis of Femtech. IEEE Transactions on Technology and Society 3, 2 (2022), 121–131

    2022

  20. [28]

    Molly Cook Escobar, Amy Schoenfeld Walker, Allison McCann, Scott Reinhard, and Helmuth Rosales. 2024. 171,000 Traveled for Abortions Last Year. See Where They Went. The New York Times (Jun 2024). https://www.nytimes.com/interactive/2024/06/13/us/abortion-state-laws-ban-travel.html

    2024

  21. [29]

    Exodus. [n. d.]. Exodus Privacy — exodus-privacy.eu.org. https://exodus-privacy.eu.org/en/

  22. [30]

    Richard J Fehring and Mary Schneider. 2017. Effectiveness of a natural family planning service program. MCN: The American Journal of Maternal/Child Nursing 42, 1 (2017), 43–49

    2017

  23. [31]

    Adrienne Porter Felt, Erika Chin, Steve Hanna, Dawn Song, and David Wagner. 2011. Android permissions demystified. In Proceedings of the 18th ACM conference on Computer and communications security. 627–638

    2011

  24. [32]

    femtech.health. 2021. FemTech Market Overview | FemTech Analytics — femtech.health. https://www.femtech.health/femtech-market-overview

    2021

  25. [33]

    fpc. [n. d.]. Fair Information Practice Principles (FIPPs). https://www.fpc.gov/resources/fipps/

  26. [34]

    FTC. [n. d.]. Mobile Advertising Network InMobi Settles FTC Charges It Tracked Hundreds of Millions of Consumers’ Locations Without Permission — ftc.gov. https://www.ftc.gov/news-events/news/press-releases/2016/06/mobile-advertising-network-inmobi-settles-ftc-charges-it-tracke...

    2016

  27. [35]

    Kulleni Gebreyes, Jennifer Radin, Asif Dhar, Jay Bhatt, Wendy Gerhardt, and Leslie Korenda. 2024. What’s causing US women to skip or delay medical care? https://www2.deloitte.com/us/en/insights/industry/health-care/why-women-skip-or-delay-health-care.html Unveiling Privacy and...

    2024

  28. [36]

    Github. [n. d.]. GitHub - androguard/androguard: Reverse engineering and pentesting for Android applications — github.com. https://github.com/ androguard/androguard

  29. [37]

    Muhammad Hassan and Masooda Bashir. 2023. Unveiling Privacy Measures in Mental Health Applications. In Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing. 648–654

    2023

  30. [38]

    Bastian Herre, Veronika Samborska, Pablo Arriagada, and Hannah Ritchie. 2023. Women’s Rights. Our World in Data (2023). https://ourworldindata.org/women-rights

    2023

  31. [39]

    The White House. 2024. Statement from President Joe Biden on Final Rule to Strengthen Privacy Protections for Reproductive Health Care. https://www.whitehouse.gov/briefing-room/statements-releases/2024/04/22/statement-from-president-joe-biden-on-final-rule-to- strengthen-priva...

    2024

  32. [40]

    icpsr. [n. d.]. Informed Consent Language for Data Sharing. https://www.icpsr.umich.edu/web/pages/datamanagement/confidentiality/conf- language.html

  33. [41]

    National Literacy Institute. 2024. Literacy Statistics 2022-2023. https://www.thenationalliteracyinstitute.com/post/literacy-statistics-2022-2023

    2024

  34. [42]

    Noël James and Caroline Kapp. 2023. Women This Week: Women in the United States Are Losing Access to Reproductive Health Services. https://www.cfr.org/blog/women-week-women-united-states-are-losing-access-reproductive-health-services

    2023

  35. [43]

    Elaine Kamarck. 2024. Abortion and the 2024 election: There is no easy way out for Republicans. https://www.brookings.edu/articles/abortion- and-the-2024-election-there-is-no-easy-way-out-for-republicans/

    2024

  36. [44]

    Monique Kelmenson. 2023. Maternal Health FemTech: A qualitative exploration of how companies serve minoritized populations in the US and what prevents them from doing more. Master’s thesis. Duke University

    2023

  37. [45]

    Jay P Kesan, Carol M Hayes, and Masooda N Bashir. 2015. A comprehensive empirical study of data privacy, trust, and consumer autonomy. Ind. LJ 91 (2015), 267

    2015

  38. [46]

    Agnieszka Kitkowska and Yefim Shulman. [n. d.]. Assessing and Combatting the Negative Effects of FemTech Dataveillance. Proceedings on Privacy Enhancing Technologies YYYY (X) 1 ([n. d.]), 3

  39. [47]

    Eleanor Klibanoff. 2023. Even after Planned Parenthood stopped performing abortions, Texas is still trying to shut it down. https://www. texastribune.org/2023/08/15/texas-abortion-planned-parenthood-lawsuit/

    2023

  40. [48]

    Tamar Krishnamurti, Mehret Birru Talabi, Lisa S Callegari, Traci M Kazmerski, and Sonya Borrero. 2022. A framework for Femtech: guiding principles for developing digital reproductive health tools in the United States. Journal of Medical Internet Research 24, 4 (2022), e36338

    2022

  41. [49]

    Rui Li, Wenrui Diao, Zhou Li, Jianqi Du, and Shanqing Guo. 2021. Android custom permissions demystified: From privilege escalation to design shortcomings. In 2021 IEEE Symposium on Security and Privacy (SP). IEEE, 70–86

    2021

  42. [50]

    Bin Liu, Deguang Kong, Lei Cen, Neil Zhenqiang Gong, Hongxia Jin, and Hui Xiong. 2015. Personalized mobile app recommendation: Reconciling app functionality and user privacy preference. In Proceedings of the eighth ACM international conference on web search and data mining. 315–324

    2015

  43. [51]

    Bin Liu, Jialiu Lin, and Norman Sadeh. 2014. Reconciling mobile app privacy and usability on smartphones: Could user privacy profiles help?. In Proceedings of the 23rd international conference on World wide web. 201–212

    2014

  44. [52]

    Patrick Liu, Katia Astudillo, Damaris Velez, Lauren Kelley, Darcey Cobbs-Lomax, and Erica S Spatz. 2020. Use of mobile health applications in low-income populations: a prospective study of facilitators and barriers. Circulation: Cardiovascular Quality and Outcomes 13, 9 (2020)...

    2020

  45. [53]

    Mary Maas. 2022. Data Privacy in the Femtech Industry — vanderbilt.edu. https://www.vanderbilt.edu/jetlaw/2022/11/08/data-privacy-in-the- femtech-industry/

    2022

  46. [54]

    Mary Madden, Michele Gilman, Karen Levy, and Alice Marwick. 2017. Privacy, poverty, and big data: A matrix of vulnerabilities for poor Americans. Wash. UL Rev. 95 (2017), 53

    2017

  47. [55]

    Arwa Mahdawi. 2019. If the government tracks women’s periods, why not track male ejaculation, too? https://www.theguardian.com/world/ commentisfree/2019/nov/02/if-the-government-tracks-womens-periods-why-not-track-male-ejaculation-too

    2019

  48. [56]

    Lisa Mekioussa Malki, Ina Kaleva, Dilisha Patel, Mark Warner, and Ruba Abu-Salma. 2024. Exploring Privacy Practices of Female mHealth Apps in a Post-Roe World. In Proceedings of the CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’24). Association...

    2024

  49. [57]

    Sharon Masling, Shane Sigel, and Megan Lipsky. 2023. Evolving Laws and Litigation Post–Dobbs: The State of Reproductive Rights. https: //www.morganlewis.com/pubs/2023/05/evolving-laws-and-litigation-post-dobbs-the-state-of-reproductive-rights-as-of-may-2023

    2023

  50. [58]

    Louise Matsakis. 2019. The WIRED Guide to Your Personal Data (and Who Is Using It). https://www.wired.com/story/wired-guide-personal-data- collection/

    2019

  51. [59]

    Maryam Mehrnezhad and Teresa Almeida. 2021. Caring for intimate data in fertility technologies. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–11

    2021

  52. [60]

    Maryam Mehrnezhad and Teresa Almeida. 2021. Caring for Intimate Data in Fertility Technologies. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 409, 11 ...

    2021

  53. [61]

    Deepa Narwani. 2023. Unlocking the full potential of Femtech in addressing women’s health needs. https://insights.omnia-health.com/technology/ unlocking-full-potential-femtech-addressing-womens-health-needs

    2023

  54. [62]

    Condé Nast. [n. d.]. Facebook Changes Its Ad Tech to Stop Discrimination — wired.com. https://www.wired.com/story/facebook-advertising- discrimination-settlement/ 24 Hassan et al. Manuscript submitted to ACM

  55. [63]

    Stephanie Nguyen. 2020. Reproductive Health Apps: A Digital Standard Case Study - Innovation at Consumer Reports. https://innovation.stage. consumerreports.org/reproductive-health-apps-a-digital-standard-case-study/

    2020

  56. [64]

    Supreme Court of the United States. 2022. Dobbs v. Jackson Women’s Health Organization. https://supreme.justia.com/cases/federal/us/597/19- 1392/#tab-opinion-4600822 Accessed: 2023-05-01

    2022

  57. [65]

    Amy Olivero. 2022. Privacy and digital health data: The femtech challenge — iapp.org. https://iapp.org/news/a/privacy-and-digital-health-data- the-femtech-challenge/

    2022

  58. [66]

    Kenneth Olmstead and Michelle Atkinson. [n. d.]. Apps Permissions in the Google Play Store — pewresearch.org. https://www.pewresearch.org/ internet/2015/11/10/apps-permissions-in-the-google-play-store/

    2015

  59. [67]

    Kristen O’Loughlin, Martha Neary, Elizabeth C Adkins, and Stephen M Schueller. 2019. Reviewing the data security and privacy policies of mobile apps for depression. Internet interventions 15 (2019), 110–115

    2019

  60. [68]

    PewResearch. [n. d.]. Chapter 3: An Analysis of Android App Permissions — pewresearch.org. https://www.pewresearch.org/internet/2015/11/10/an- analysis-of-android-app-permissions/

    2015

  61. [69]

    Morteza Safaei Pour, Christelle Nader, Kurt Friday, and Elias Bou-Harb. 2023. A Comprehensive Survey of Recent Internet Measurement Techniques for Cyber Security. Computers & Security (2023), 103123

    2023

  62. [70]

    Anya ER Prince. 2023. Reproductive health surveillance. BCL Rev. 64 (2023), 1077

    2023

  63. [71]

    Nicole Rapfogel and Kierra B. Jones. 2024. Project 2025 Would Take Away Access to Free Emergency Contraception for 48 Million Women. https://www.americanprogress.org/article/project-2025-would-take-away-access-to-free-emergency-contraception-for-48-million-women/

    2024

  64. [72]

    Pernille Ravn Jakobsen, Anne Pernille Hermann, Jens Søndergaard, Uffe Kock Wiil, and Jane Clemensen. 2018. Development of an mHealth application for women newly diagnosed with osteoporosis without preceding fractures: a participatory design approach. International journal of e...

    2018

  65. [73]

    reproductiverights.org. [n. d.]. The Case in Depth - Center for Reproductive Rights. https://reproductiverights.org/case/scotus-mississippi- abortion-ban/dobbs-jackson-womens-health

  66. [74]

    Pew Research. 2024. 4. Gender, family, reproductive issues and the 2024 election. https://www.pewresearch.org/politics/2024/06/06/gender-family- reproductive-issues-and-the-2024-election/

    2024

  67. [75]

    Celia Rosas. 2019. The future is femtech: Privacy and data security issues surrounding femtech applications. Hastings Bus. LJ 15 (2019), 319

    2019

  68. [76]

    Donna Rosato. 2020. What Your Period Tracker App Knows About You - Consumer Reports. https://www.consumerreports.org/health- privacy/what-your-period-tracker-app-knows-about-you-a8701683935/

    2020

  69. [77]

    Allysan Scatterday. 2021. This is no ovary-action: femtech apps need stronger regulations to protect data and advance public health goals. NCJL & Tech. 23 (2021), 636

    2021

  70. [78]

    Dylan Scott. 2022. It’s not just about abortion. https://www.vox.com/23137822/abortion-birth-control-health-care-roe-wade

    2022

  71. [79]

    Ahmed Sherif. 2024. Mobile OS market share worldwide 2009-2023 | Statista — statista.com. https://www.statista.com/statistics/272698/global- market-share-held-by-mobile-operating-systems-since-2009/

    2024

  72. [80]

    Laura Shipp and Jorge Blasco. 2020. How private is your period?: A systematic analysis of menstrual app privacy policies. Proc. Priv. Enhancing Technol. 2020, 4 (2020), 491–510

    2020

  73. [81]

    Laura Shipp and Jorge Blasco. 2020. How private is your period?: A systematic analysis of menstrual app privacy policies. Proceedings on Privacy Enhancing Technologies 2020, 4 (Aug 2020), 491–510. https://doi.org/10.2478/popets-2020-0083

    2020 doi

  74. [82]

    Anna Spoerre. 2024. Bill ending Medicaid reimbursements to Planned Parenthood clears Missouri Senate • Missouri Independent. https: //missouriindependent.com/2024/04/10/missouri-planned-parenthood-abortion-medicaid-reimbusements/

    2024

  75. [83]

    SUPREME COURT OF THE UNITED STATES. 2022. Dobbs v. Jackson Women’s Health Organization. SUPREME COURT. 1–213 pages. https: //www.supremecourt.gov/opinions/21pdf/19-1392_6j37.pdf

    2022

  76. [84]

    Statista. [n. d.]. https://www.statista.com/study/116210/femtech/

  77. [85]

    Conor Stewart. 2024. Countries to have received the highest amount of investment in the femtech industry as of 2022. https://www.statista.com/ statistics/1320410/leading-countries-for-femtech-investment/

    2024

  78. [86]

    Conor Stewart. 2024. Femtech companies worldwide in 2022, by region. https://www.statista.com/statistics/1266393/femtech-companies- worldwide-in-2021-by-region/

    2024

  79. [87]

    Vinithasree Subbhuraam. 2021. FemTech solutions for advancing women’s health. In Predictive Analytics in Healthcare, Volume 1: Transforming the future of medicine. IOP Publishing

    2021

  80. [88]

    Ali Sunyaev, Tobias Dehling, Patrick L Taylor, and Kenneth D Mandl. 2015. Availability and quality of mobile health app privacy policies. Journal of the American Medical Informatics Association 22, e1 (2015), e28–e33

    2015

  81. [89]

    Melissa Suran. 2022. Treating cancer in pregnant patients after Roe v Wade overturned. JAMA 328, 17 (2022), 1674–1676

    2022

  82. [90]

    Madeline Y Sutton, Ngozi F Anachebe, Regina Lee, and Heather Skanes. 2021. Racial and ethnic disparities in reproductive health services and outcomes, 2020. Obstetrics & Gynecology 137, 2 (2021), 225–233

    2021

  83. [91]

    Symantec. [n. d.]. App Analytics SDKs Could Expose Sensitive Data — symantec-enterprise-blogs.security.com. https://symantec-enterprise- blogs.security.com/blogs/expert-perspectives/app-analytics-sdks-could-expose-sensitive-data

  84. [92]

    Mir Sajjad Hussain Talpur, Murtaza Hussain Shaikh, Riaz Ali Buriro, Hira Sajjad Talpur, Fozia Talpur, Hina Shafi, and Munazza Ahsan Shaikh. 2014. Internet of things as intimating for pregnant women’s healthcare: an impending privacy issues. TELKOMNIKA Indonesian Journal of Ele...

    2014

  85. [93]

    taylorandfrancis. [n. d.]. A short introduction to data sharing ethics. https://authorservices.taylorandfrancis.com/data-sharing/data-sharing-ethics/

  86. [94]

    Terri-ann Monique Thompson, Yves-Yvette Young, Tanya M Bass, Stephanie Baker, Oriaku Njoku, Jessica Norwood, and Monica Simpson. 2022. Racism Runs Through It: Examining The Sexual And Reproductive Health Experience Of Black Women In The South: Study examines the sexual and rep...

    2022

  87. [95]

    Rachel A Tinius, Mackenzie Polston, Hannah Bradshaw, Paula Ashley, Ashley Greene, and Angel N Parker. 2021. An assessment of mobile applications designed to address physical activity during pregnancy and postpartum. International Journal of Exercise Science 14, 7 (2021), 382

    2021

  88. [96]

    Nina Totenberg and Sarah McCammon. 2022. Supreme Court overturns Roe v. Wade, ending right to abortion upheld for decades. https: //www.npr.org/2022/06/24/1102305878/supreme-court-abortion-roe-v-wade-decision-overturn

    2022

  89. [97]

    Kelly Treder, Katharine O White, Elisabeth Woodhams, Rushina Pancholi, and Leanne Yinusa-Nyahkoon. 2022. Racism and the reproductive health experiences of US-born Black women. Obstetrics & Gynecology 139, 3 (2022), 407–416

    2022

  90. [98]

    Beatrice Tylstedt, Maria Normark, and Lina Eklund. 2023. Reimagining the cycle: interaction in self-tracking period apps and menstrual empowerment. Frontiers in Computer Science 5 (2023)

    2023

  91. [99]

    Utrech University. [n. d.]. Informed consent for data sharing. https://www.uu.nl/en/research/research-data-management/guides/informed- consent-for-data-sharing

  92. [100]

    Jessica Valenti. 2015. It isn’t justice for Purvi Patel to serve 20 years in prison for an abortion | Jessica Valenti. https://www.theguardian.com/ commentisfree/2015/apr/02/it-isnt-justice-for-purvi-patel-to-serve-20-years-in-prison-for-an-abortion

    2015

  93. [101]

    Paul Voigt and Axel Von dem Bussche. 2017. The eu general data protection regulation (gdpr). A Practical Guide, 1st Ed., Cham: Springer International Publishing 10, 3152676 (2017), 10–5555

    2017

  94. [102]

    Katherine Ward and Angela Deneris. 2018. An update on menopause management. Journal of Midwifery & Women’s Health 63, 2 (2018), 168–177

    2018

  95. [103]

    Brenda K Wiederhold. 2021. Femtech: digital help for women’s health care across the life span. Cyberpsychology, Behavior, and Social Networking 24, 11 (2021), 697–698

    2021

  96. [104]

    Moryel Yashar and Sabrina Wannon. 2022. Future Flora as a Case Study for FemTech’s Role in Science: Tackling the Taboo Head-On. UCLA Undergraduate Research Journal for the Humanities and Social SciencesVolume 19 (2022). A Policy Coding This section includes our codes for the p...

    2022

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