PrivacyAssist: A User-Centric Agent Framework for Detecting Privacy Inconsistencies in Android Apps

• URL: http://arxiv.org/abs/2604.23248v1
• Date: Sat, 25 Apr 2026 10:58:05 GMT
• Title: PrivacyAssist: A User-Centric Agent Framework for Detecting Privacy Inconsistencies in Android Apps
• Abstract summary: This paper presents PrivacyAssist, a multi-agent LLM-based platform that detects inconsistencies between user-granted permissions and developers' declared sensitive data collection and sharing practices.<n>We evaluate PrivacyAssist with 200 users and 2,347 Android apps, finding that only 16% of apps are fully consistent between granted permissions and declared data practices.
• Score: 0.9892966978799635
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mobile apps offer significant benefits, but their privacy protections often remain ineffective and confusing for users. While prior work mainly analyzes app privacy vulnerabilities, few approaches help users understand, set, and enforce their privacy preferences. This paper presents PrivacyAssist, a multi-agent LLM-based platform that detects inconsistencies between user-granted permissions and developers' declared sensitive data collection and sharing practices. Using Retrieval-Augmented Generation (RAG), PrivacyAssist provides concise explanations and real-time on-device warnings to support informed installation decisions. We evaluate PrivacyAssist with 200 users and 2,347 Android apps, finding that only 16% of apps are fully consistent between granted permissions and declared data practices.

Related papers

• MemPrivacy: Privacy-Preserving Personalized Memory Management for Edge-Cloud Agents [58.65181747637314]
  We propose MemPrivacy, which identifies privacy-sensitive spans on edge devices.<n>It replaces them with semantically structured type-aware placeholders for cloud-side memory processing.<n>MemPrivacy minimizes sensitive data exposure while retaining the information required for effective memory formation and retrieval.
  arXiv  Detail & Related papers  (2026-05-10T13:31:58Z)
• 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)
• Protecting Users From Themselves: Safeguarding Contextual Privacy in Interactions with Conversational Agents [33.26308626066122]
  We characterize the notion of contextual privacy for user interactions with Conversational Agents (LCAs)<n>It aims to minimize privacy risks by ensuring that users (sender) disclose only information that is both relevant and necessary for achieving their intended goals.<n>We propose a locally deployable framework that operates between users and LCAs, identifying and reformulating out-of-context information in user prompts.
  arXiv  Detail & Related papers  (2025-02-22T09:05:39Z)
• Privacy Bills of Materials: A Transparent Privacy Information Inventory for Collaborative Privacy Notice Generation in Mobile App Development [23.41168782020005]
  We introduce PriBOM, a systematic software engineering approach to better capture and coordinate mobile app privacy information.<n>PriBOM facilitates transparency-centric privacy documentation and specific privacy notice creation, enabling traceability and trackability of privacy practices.
  arXiv  Detail & Related papers  (2025-01-02T08:14:52Z)
• On the Differential Privacy and Interactivity of Privacy Sandbox Reports [78.85958224681858]
  The Privacy Sandbox initiative from Google includes APIs for enabling privacy-preserving advertising functionalities.<n>We provide an abstract model for analyzing the privacy of these APIs and show that they satisfy a formal DP guarantee.
  arXiv  Detail & Related papers  (2024-12-22T08:22:57Z)
• Rescriber: Smaller-LLM-Powered User-Led Data Minimization for LLM-Based Chatbots [2.2447085410328103]
  Rescriber is a browser extension that supports user-led data minimization in LLM-based conversational agents.<n>Our studies showed that Rescriber helped users reduce unnecessary disclosure and addressed their privacy concerns.<n>Our findings confirm the viability of smaller-LLM-powered, user-facing, on-device privacy controls.
  arXiv  Detail & Related papers  (2024-10-10T01:23:16Z)
• Assessing Privacy Compliance of Android Third-Party SDKs [16.975384208528972]
  Third-party Software Development Kits (SDKs) are widely adopted in Android app development.<n>This convenience raises substantial concerns about unauthorized access to users' privacy-sensitive information.<n>Our study offers a targeted analysis of user privacy protection among Android third-party SDKs.
  arXiv  Detail & Related papers  (2024-09-16T15:44:43Z)
• Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning [62.224804688233]
  differential privacy (DP) offers a promising solution by ensuring models are 'almost indistinguishable' with or without any particular privacy unit. We study user-level DP motivated by applications where it necessary to ensure uniform privacy protection across users.
  arXiv  Detail & Related papers  (2024-06-20T13:54:32Z)
• In Pursuit of Privacy: The Value-Centered Privacy Assistant [2.995126929132991]
  We develop a prototype smartphone value-centered privacy assistant (VcPA)<n>VcPA promotes user privacy decisions based on personal values.<n>We establish proof-of-concept that a VcPA helps users make more value-centered app choices.
  arXiv  Detail & Related papers  (2023-08-10T17:04:12Z)
• 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)

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.