Do Privacy Policies Match with the Logs? An Empirical Study of Privacy Disclosure in Android Application Logs
- URL: http://arxiv.org/abs/2604.18552v1
- Date: Mon, 20 Apr 2026 17:43:25 GMT
- Title: Do Privacy Policies Match with the Logs? An Empirical Study of Privacy Disclosure in Android Application Logs
- Abstract summary: We analyzed 1,000 Android apps across multiple categories, generating 86,836,964 log entries.<n>Our findings reveal that while most applications (88.0%) provide privacy policies, only 28.5% explicitly mention logging practices.<n>Among those that reference logging, most clearly describe what information is logged; however, 27.7% of log-related statements remain overly simplistic or vague.
- Score: 8.897161752364116
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Privacy policies are intended to inform users about how software systems collect and handle data, yet they often remain vague or incomplete. This paper presents an empirical study of patterns in log-related statements within privacy policies and their alignment with privacy disclosures observed in Android application logs. We analyzed 1,000 Android apps across multiple categories, generating 86,836,964 log entries. Our findings reveal that while most applications (88.0%) provide privacy policies, only 28.5% explicitly mention logging practices. Among those that reference logging, most clearly describe what information is logged; however, 27.7% of log-related statements remain overly simplistic or vague, offering limited insight into actual data collection. We further observed widespread privacy leakages in application logs, with 67.6% of apps leaking sensitive information not mentioned in their policies. Alarmingly, only 4% of applications demonstrated consistent alignment between declared policy contents and actual logged data. These findings highlight that current privacy policies provide incomplete or ambiguous descriptions of logging practices, which frequently do not align with actual logging behaviors.
Related papers
- Do Phone-Use Agents Respect Your Privacy? [97.81424230136075]
We introduce MyPhoneBench, a verifiable evaluation framework for privacy behavior in mobile agents.<n>We operationalize privacy-respecting phone use as permissioned access, minimal disclosure, and user-controlled memory through a minimal privacy contract.<n>Across five frontier models on 10 mobile apps and 300 tasks, we find that task success, privacy-compliant task completion, and later-session use of saved preferences are distinct capabilities.
arXiv Detail & Related papers (2026-04-01T14:50:50Z) - PrivPRISM: Automatically Detecting Discrepancies Between Google Play Data Safety Declarations and Developer Privacy Policies [8.052271364177988]
Self-declared disclosures often contradict the full privacy policies, deceiving users about actual data practices.<n>We introduce PrivPRISM, a robust framework that combines encoder and decoder language models to extract and compare fine-grained data practices from privacy policies.<n>Our findings expose systemic issues, including widespread reuse of generic privacy policies, vague / contradictory statements, and hidden risks in high-profile apps with 100M+ downloads.
arXiv Detail & Related papers (2026-03-10T05:33:40Z) - Is Your Private Information Logged? An Empirical Study on Android App Logs [9.557846787437379]
We build a comprehensive dataset of Android app logs and conduct an empirical study to analyze the status and severity of privacy leaks in Android app logs.<n>Our study reveals five different categories of concerns from real-world developers regarding privacy issues related to software logs and the prevalence of privacy leaks in Android app logs.
arXiv Detail & Related papers (2026-02-08T09:56:48Z) - MAGPIE: A dataset for Multi-AGent contextual PrIvacy Evaluation [54.410825977390274]
Existing benchmarks to evaluate contextual privacy in LLM-agents primarily assess single-turn, low-complexity tasks.<n>We first present a benchmark - MAGPIE comprising 158 real-life high-stakes scenarios across 15 domains.<n>We then evaluate the current state-of-the-art LLMs on their understanding of contextually private data and their ability to collaborate without violating user privacy.
arXiv Detail & Related papers (2025-06-25T18:04:25Z) - PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action [54.11479432110771]
PrivacyLens is a novel framework designed to extend privacy-sensitive seeds into expressive vignettes and further into agent trajectories.<n>We instantiate PrivacyLens with a collection of privacy norms grounded in privacy literature and crowdsourced seeds.<n>State-of-the-art LMs, like GPT-4 and Llama-3-70B, leak sensitive information in 25.68% and 38.69% of cases, even when prompted with privacy-enhancing instructions.
arXiv Detail & Related papers (2024-08-29T17:58:38Z) - Honesty is the Best Policy: On the Accuracy of Apple Privacy Labels Compared to Apps' Privacy Policies [13.771909487087793]
Apple introduced privacy labels in Dec. 2020 as a way for developers to report the privacy behaviors of their apps.
While Apple does not validate labels, they also require developers to provide a privacy policy, which offers an important comparison point.
We fine-tuned BERT-based language models to extract privacy policy features for 474,669 apps on the iOS App Store.
arXiv Detail & Related papers (2023-06-29T16:10:18Z) - ATLAS: Automatically Detecting Discrepancies Between Privacy Policies
and Privacy Labels [2.457872341625575]
We introduce the Automated Privacy Label Analysis System (ATLAS)
ATLAS identifies possible discrepancies between mobile app privacy policies and their privacy labels.
We find that, on average, apps have 5.32 such potential compliance issues.
arXiv Detail & Related papers (2023-05-24T05:27:22Z) - The Overview of Privacy Labels and their Compatibility with Privacy
Policies [24.871967983289117]
Privacy nutrition labels provide a way to understand an app's key data practices without reading the long and hard-to-read privacy policies.
Apple and Google have implemented mandates requiring app developers to fill privacy nutrition labels highlighting their privacy practices.
arXiv Detail & Related papers (2023-03-14T20:10:28Z) - Privacy Explanations - A Means to End-User Trust [64.7066037969487]
We looked into how explainability might help to tackle this problem.
We created privacy explanations that aim to help to clarify to end users why and for what purposes specific data is required.
Our findings reveal that privacy explanations can be an important step towards increasing trust in software systems.
arXiv Detail & Related papers (2022-10-18T09:30:37Z) - Analysis of Longitudinal Changes in Privacy Behavior of Android
Applications [79.71330613821037]
In this paper, we examine the trends in how Android apps have changed over time with respect to privacy.
We examine the adoption of HTTPS, whether apps scan the device for other installed apps, the use of permissions for privacy-sensitive data, and the use of unique identifiers.
We find that privacy-related behavior has improved with time as apps continue to receive updates, and that the third-party libraries used by apps are responsible for more issues with privacy.
arXiv Detail & Related papers (2021-12-28T16:21:31Z) - Towards practical differentially private causal graph discovery [74.7791110594082]
Causal graph discovery refers to the process of discovering causal relation graphs from purely observational data.
We present a differentially private causal graph discovery algorithm, Priv-PC, which improves both utility and running time compared to the state-of-the-art.
arXiv Detail & Related papers (2020-06-15T18:30:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.