# Beyond Permissions: Investigating Mobile Personalization with Simulated Personas

arXiv: https://arxiv.org/abs/2511.01336 · HTML: https://mlsanity.com/paper/2511.01336/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 45 references that are in the map.
- First posted on arXiv 3 Nov 2025.

## Builds on (most cited first, 9 of 9)

- [Protecting Sensory Data against Sensitive Inferences](https://mlsanity.com/paper/1802.07802/) (2018)
- [Towards a Design Guideline for RPA Evaluation: A Survey of Large Language Model-Based Role-Playing Agents](https://mlsanity.com/paper/2502.13012/) (2025)
- [A Survey of Privacy Vulnerabilities of Mobile Device Sensors](https://mlsanity.com/paper/2106.10154/) (2021)
- [An Empathy-Based Sandbox Approach to Bridge the Privacy Gap among Attitudes, Goals, Knowledge, and Behaviors](https://mlsanity.com/paper/2309.14510/) (2023)
- [Why am I seeing this: Democratizing End User Auditing for Online Content Recommendations](https://mlsanity.com/paper/2410.04917/) (2024)
- [Mobile App Privacy in Software Engineering Research: A Systematic Mapping Study](https://mlsanity.com/paper/1910.03622/) (2019)
- [Mitigating Location Privacy Attacks on Mobile Devices using Dynamic App Sandboxing](https://mlsanity.com/paper/1808.04490/) (2018)
- [Data Safety vs. App Privacy: Comparing the Usability of Android and iOS Privacy Labels](https://mlsanity.com/paper/2312.03918/) (2023)
- [VBIT: Towards Enhancing Privacy Control Over IoT Devices](https://mlsanity.com/paper/2409.06233/) (2024)

## Abstract (arXiv)

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.