用虚拟人格模拟用户行为，看懂手机应用如何个性化推荐。
Beyond Permissions: Investigating Mobile Personalization with Simulated Personas
- 用真实传感器数据生成虚拟用户画像，实时注入安卓设备
- 发现健身、电商、天气等应用对行为变化有明显响应差异
- 适合关注隐私透明与用户自主权的研究者和开发者
移动应用越来越多地依赖传感器数据推断用户上下文并提供个性化服务，但其个性化机制对用户和研究者而言仍不透明。本文提出一个沙箱系统，通过传感器欺骗和人格模拟来审计与可视化应用对推断行为的反应。我们不将欺骗视为攻击，而是作为行为透明与用户赋权的工具。系统实时注入基于生活方式的多传感器画像到安卓设备中，使用户可观测应用在高活动、位置变化或时间变化等情境下的响应。结合自动截图与GPT-4 Vision的UI摘要，可记录细微的个性化线索。初步结果显示，在健身、电商及日常服务类应用（如天气、导航）中存在可测量的适应性变化。本工具包为隐私增强技术与面向用户的透明干预提供了基础。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。