Beyond Permissions: Investigating Mobile Personalization with Simulated Personas

• URL: http://arxiv.org/abs/2511.01336v1
• Date: Mon, 03 Nov 2025 08:39:38 GMT
• Title: Beyond Permissions: Investigating Mobile Personalization with Simulated Personas
• Abstract summary: This paper presents a sandbox system that uses sensor spoofing and persona simulation to audit and visualize how mobile apps respond to inferred behaviors.<n>Our system injects multi-sensor profiles into Android devices in real time, enabling users to observe app responses to contexts such as high activity, location shifts, or time-of-day changes.<n>Preliminary findings show measurable app adaptations across fitness, e-commerce, and everyday service apps such as weather and navigation.
• Score: 30.156063920321774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mobile applications increasingly rely on sensor data to infer user context and deliver personalized experiences. Yet the mechanisms behind this personalization remain opaque to users and researchers alike. This paper presents a sandbox system that uses sensor spoofing and persona simulation to audit and visualize how mobile apps respond to inferred behaviors. Rather than treating spoofing as adversarial, we demonstrate its use as a tool for behavioral transparency and user empowerment. Our system injects multi-sensor profiles - generated from structured, lifestyle-based personas - into Android devices in real time, enabling users to observe app responses to contexts such as high activity, location shifts, or time-of-day changes. With automated screenshot capture and GPT-4 Vision-based UI summarization, our pipeline helps document subtle personalization cues. Preliminary findings show measurable app adaptations across fitness, e-commerce, and everyday service apps such as weather and navigation. We offer this toolkit as a foundation for privacy-enhancing technologies and user-facing transparency interventions.

Related papers

• When Agents See Differently: Exposing UI Desynchronization Threats in Mobile Agents [31.736012043129495]
  We show that users and agents observe consistent information from the same interface.<n>The same UI state can present materially different information to users and agents, a mismatch we term human-agent UI desynchronization.<n>We develop an automated framework that constructs user runtime instruction-agnostic UI desynchronization attacks and realizes them in deployable APKs.
  arXiv  Detail & Related papers  (2026-09-15T07:02:10Z)
• Expecting (Targeted Ads)? Network Analysis of User Health Data Leakage in Fertility Tracking Apps [9.426024604008406]
  We conduct a network-based measurement study of a corpus of 20 Android fertility tracking apps from the Google Play Store.<n>We identify explicit leakage of user health data as well implicit leakage through highly targeted contextual advertising URL's.<n>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.
  arXiv  Detail & Related papers  (2026-06-24T18:17:04Z)
• VitaBench 2.0: Evaluating Personalized and Proactive Agents in Long-Term User Interactions [63.13827503828231]
  We introduce VitaBench 2.0, a benchmark for evaluating personalized and proactive agent behavior in long-term user interactions.<n>Results show that real-world personalization remains highly challenging even for state-of-the-art models.
  arXiv  Detail & Related papers  (2026-05-26T15:07:38Z)
• An Empirical Analysis of Mobile Energy Consumption Across User Configurations [0.0]
  This research aims to bridge the gap between technical energy analysis and practical user application by quantifying the energy consumption of different user-controlled parameters.<n>The objective is to have a systematic evaluation of the energy impact of user-controllable factors, including device settings.<n>By analyzing over 12,000 data points, this paper quantifies the real-world impact of common settings, revealing the trade-offs between user experience and device autonomy.
  arXiv  Detail & Related papers  (2026-04-28T12:54:30Z)
• A Big Step Forward? A User-Centric Examination of iOS App Privacy Report and Enhancements [11.535543802697836]
  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.
  arXiv  Detail & Related papers  (2025-11-01T09:29:04Z)
• FPI-Det: a face--phone Interaction Dataset for phone-use detection and understanding [20.181223336698675]
  Mobile devices have created new challenges for vision systems in safety monitoring, workplace productivity assessment, and attention management.<n>We introduce the FPI-Det, containing 22,879 images with synchronized annotations for faces and phones across workplace, education, transportation, and public scenarios.
  arXiv  Detail & Related papers  (2025-09-11T02:50:03Z)
• Screencast-Based Analysis of User-Perceived GUI Responsiveness [53.53923672866705]
  tool is a technique that measures GUI responsiveness directly from mobile screencasts.<n>It uses computer vision to detect user interactions and analyzes frame-level visual changes to compute two key metrics.<n>tool has been deployed in an industrial testing pipeline and analyzes thousands of screencasts daily.
  arXiv  Detail & Related papers  (2025-08-02T12:13:50Z)
• PersonaAgent: When Large Language Model Agents Meet Personalization at Test Time [87.99027488664282]
  PersonaAgent is a framework designed to address versatile personalization tasks.<n>It integrates a personalized memory module and a personalized action module.<n>Test-time user-preference alignment strategy ensures real-time user preference alignment.
  arXiv  Detail & Related papers  (2025-06-06T17:29:49Z)
• Creating General User Models from Computer Use [53.59999173952482]
  This paper presents an architecture for a general user model (GUM) that learns about you by observing any interaction you have with your computer.<n>The GUM takes as input any unstructured observation of a user (e.g., device screenshots) and constructs confidence-weighted propositions that capture user knowledge and preferences.
  arXiv  Detail & Related papers  (2025-05-16T04:00:31Z)
• Advancing Mobile UI Testing by Learning Screen Usage Semantics [0.42303492200814446]
  This research seeks to enhance automated UI testing techniques by learning the screen usage semantics of mobile apps.<n>It also improves the usability of a mobile app's interface by identifying and mitigating UI design issues.
  arXiv  Detail & Related papers  (2025-05-15T01:40:43Z)
• 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)
• 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)
• Emerging App Issue Identification via Online Joint Sentiment-Topic Tracing [66.57888248681303]
  We propose a novel emerging issue detection approach named MERIT. Based on the AOBST model, we infer the topics negatively reflected in user reviews for one app version. Experiments on popular apps from Google Play and Apple's App Store demonstrate the effectiveness of MERIT.
  arXiv  Detail & Related papers  (2020-08-23T06:34:05Z)
• Assisted Perception: Optimizing Observations to Communicate State [112.40598205054994]
  We aim to help users estimate the state of the world in tasks like robotic teleoperation and navigation with visual impairments. We synthesize new observations that lead to more accurate internal state estimates when processed by the user.
  arXiv  Detail & Related papers  (2020-08-06T19:08:05Z)
• General-Purpose User Embeddings based on Mobile App Usage [46.343844014289246]
  behaviors on mobile app usage, including retention, installation, and uninstallation, can be a good indicator for both long-term and short-term interests of users. Traditionally, user modeling from mobile app usage heavily relies on handcrafted feature engineering. We present a tailored AutoEncoder-coupled Transformer Network (AETN), by which we overcome these challenges and achieve the goals of reducing manual efforts and boosting performance.
  arXiv  Detail & Related papers  (2020-05-27T12:01:50Z)

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.