Beyond Permissions: Investigating Mobile Personalization with Simulated Personas
This addresses the problem of transparency in mobile personalization for users and researchers, offering a tool for behavioral transparency and user empowerment, though it is incremental in building on existing spoofing techniques.
The paper tackles the opacity of mobile app personalization by developing a sandbox system that uses sensor spoofing and persona simulation to audit and visualize how apps respond to inferred behaviors, with preliminary findings showing measurable app adaptations across various app categories.
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.