A Big Step Forward? A User-Centric Examination of iOS App Privacy Report and Enhancements

• URL: http://arxiv.org/abs/2511.00467v1
• Date: Sat, 01 Nov 2025 09:29:04 GMT
• Title: A Big Step Forward? A User-Centric Examination of iOS App Privacy Report and Enhancements
• Authors: Liu Wang, Dong Wang, Shidong Pan, Zheng Jiang, Haoyu Wang, Yi Wang,
• Abstract summary: Apple introduced a new feature since iOS 15.2, App Privacy Report, to inform users about detailed insights into apps' data access and sharing.<n>This feature has been marketed as a big step forward in user privacy, but its real-world impacts on user privacy and control remain unexamined.<n>This work provides practical insights that could help enhance user privacy transparency.
• Score: 11.535543802697836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The prevalent engagement with mobile apps underscores the importance of understanding their data practices. Transparency plays a crucial role in this context, ensuring users to be informed and give consent before any data access occurs. Apple introduced a new feature since iOS 15.2, App Privacy Report, to inform users about detailed insights into apps' data access and sharing. This feature continues Apple's trend of privacy-focused innovations (following Privacy Nutrition Labels), and has been marketed as a big step forward in user privacy. However, its real-world impacts on user privacy and control remain unexamined. We thus proposed an end-to-end study involving systematic assessment of the App Privacy Report's real-world benefits and limitations, LLM-enabled and multi-technique synthesized enhancements, and comprehensive evaluation from both system and user perspectives. Through a structured focus group study with twelve everyday iOS users, we explored their experiences, understanding, and perceptions of the feature, suggesting its limited practical impact resulting from missing important details. We identified two primary user concerns: the clarity of data access purpose and domain description. In response, we proposed enhancements including a purpose inference framework and domain clarification pipeline. We demonstrated the effectiveness and benefits of such enhancements for mobile app users. This work provides practical insights that could help enhance user privacy transparency and discusses areas for future research.

Related papers

• Exploring the Effectiveness of Google Play Store's Privacy Transparency Channels [7.162422068114824]
  The Google Play Store requires Android developers to more responsibly communicate their apps' privacy practices to potential users.<n>It is unclear how effective those channels are in helping users make informed decisions in the app selection and installation process.<n>We conducted a study for 190 participants to interact with our simulated privacy transparency channels of mobile apps.
  arXiv  Detail & Related papers  (2025-11-17T16:40:19Z)
• Understanding Users' Security and Privacy Concerns and Attitudes Towards Conversational AI Platforms [3.789219860006095]
  We conduct a large-scale analysis of over 2.5M user posts from the r/ChatGPT Reddit community to understand users' security and privacy concerns.<n>We find that users are concerned about each stage of the data lifecycle (i.e., collection, usage, and retention)<n>We provide recommendations for users, platforms, enterprises, and policymakers to enhance transparency, improve data controls, and increase user trust and adoption.
  arXiv  Detail & Related papers  (2025-04-09T03:22:48Z)
• 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)
• Collection, usage and privacy of mobility data in the enterprise and public administrations [55.2480439325792]
  Security measures such as anonymization are needed to protect individuals' privacy. Within our study, we conducted expert interviews to gain insights into practices in the field. We survey privacy-enhancing methods in use, which generally do not comply with state-of-the-art standards of differential privacy.
  arXiv  Detail & Related papers  (2024-07-04T08:29:27Z)
• User Interaction Data in Apps: Comparing Policy Claims to Implementations [0.0]
  We analyzed the top 100 apps across diverse categories using static analysis methods to evaluate the alignment between policy claims and implemented data collection techniques. Our findings highlight the lack of transparency in data collection and the associated risk of re-identification, raising concerns about user privacy and trust.
  arXiv  Detail & Related papers  (2023-12-05T12:11:11Z)
• Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment [100.1798289103163]
  We present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP) Key points and high-level contents of the article were originated from the discussions from "Differential Privacy (DP): Challenges Towards the Next Frontier" This article aims to provide a reference point for the algorithmic and design decisions within the realm of privacy, highlighting important challenges and potential research directions.
  arXiv  Detail & Related papers  (2023-04-14T05:29:18Z)
• Protecting User Privacy in Online Settings via Supervised Learning [69.38374877559423]
  We design an intelligent approach to online privacy protection that leverages supervised learning. By detecting and blocking data collection that might infringe on a user's privacy, we can restore a degree of digital privacy to the user.
  arXiv  Detail & Related papers  (2023-04-06T05:20:16Z)
• 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)
• Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees [81.6471440778355]
  We propose a Cross-network Social User Embedding framework, namely DP-CroSUE, to learn the comprehensive representations of users in a privacy-preserving way. In particular, for each heterogeneous social network, we first introduce a hybrid differential privacy notion to capture the variation of privacy expectations for heterogeneous data types. To further enhance user embeddings, a novel cross-network GCN embedding model is designed to transfer knowledge across networks through those aligned users.
  arXiv  Detail & Related papers  (2022-09-04T06:22:37Z)
• SPAct: Self-supervised Privacy Preservation for Action Recognition [73.79886509500409]
  Existing approaches for mitigating privacy leakage in action recognition require privacy labels along with the action labels from the video dataset. Recent developments of self-supervised learning (SSL) have unleashed the untapped potential of the unlabeled data. We present a novel training framework which removes privacy information from input video in a self-supervised manner without requiring privacy labels.
  arXiv  Detail & Related papers  (2022-03-29T02:56:40Z)
• Towards Mass Adoption of Contact Tracing Apps -- Learning from Users' Preferences to Improve App Design [3.187723878624947]
  We explore user preferences for contact tracing apps using market research techniques and conjoint analysis. Our results confirm the privacy-preserving design of most European contact tracing apps. We conclude that adding goal-congruent features will play an important role in fostering mass adoption.
  arXiv  Detail & Related papers  (2020-11-24T19:08:09Z)

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.