paper-with-me

홈 › Papers

Do Phone-Use Agents Respect Your Privacy?

2026-04-01 · Zhengyang Tang, Ke Ji, Xidong Wang, Zihan Ye, Xinyuan Wang, Yiduo Guo, Ziniu Li, Chenxin Li, Jingyuan Hu, Shunian Chen, Tongxu Luo, Jiaxi Bi, Zeyu Qin, Shaobo Wang, Xin Lai, Pengyuan Lyu, Junyi Li, Can Xu, Chengquan Zhang, Han Hu, Ming Yan, Benyou Wang arxiv

We study whether phone-use agents respect privacy while completing benign mobile tasks. This question has remained hard to answer because privacy-compliant behavior is not operationalized for phone-use agents, and ordinary apps do not reveal exactly what data agents type into which form entries during execution. To make this question measurable, we introduce MyPhoneBench, a verifiable evaluation framework for privacy behavior in mobile agents. We operationalize privacy-respecting phone use as permissioned access, minimal disclosure, and user-controlled memory through a minimal privacy contract, iMy, and pair it with instrumented mock apps plus rule-based auditing that make unnecessary permission requests, deceptive re-disclosure, and unnecessary form filling observable and reproducible. Across five frontier models on 10 mobile apps and 300 tasks, we find that task success, privacy-compliant task completion, and later-session use of saved preferences are distinct capabilities, and no single model dominates all three. Evaluating success and privacy jointly reshuffles the model ordering relative to either metric alone. The most persistent failure mode across models is simple data minimization: agents still fill optional personal entries that the task does not require. These results show that privacy failures arise from over-helpful execution of benign tasks, and that success-only evaluation overestimates the deployment readiness of current phone-use agents. All code, mock apps, and agent trajectories are publicly available at~ https://github.com/FreedomIntelligence/MyPhoneBench.

📄 PDF Abstract BibTeX arXiv:2604.00986

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Mind the Third Eye! Benchmarking Privacy Awareness in MLLM-powered Smartphone Agents

2025-08-27 · Zhixin Lin, Jungang Li, Shidong Pan, Yibo Shi 외 arxiv

Smartphones bring significant convenience to users but also enable devices to extensively record various types of personal information. Existing smartphone agents powered by Multimodal Large Language Models (MLLMs) have …

Is your chatbot GDPR compliant? Open issues in agent design

2020-05-26 · Rahime Belen Saglam, Jason R. C. Nurse

Conversational agents open the world to new opportunities for human interaction and ubiquitous engagement. As their conversational abilities and knowledge has improved, these agents have begun to have access to an increa…

Chatbot

How Google Search Works

2024-09-20 · Authorea 2024 10 · Kamal Acharya

Internet telephony consists of a combination of hardware and software that enables you to use the Internet as the transmission medium for telephone calls. For users who have free, or fixed-price Internet access, Internet…

Form

Anonymization-Enhanced Privacy Protection for Mobile GUI Agents: Available but Invisible

2026-02-08 · Lepeng Zhao, Zhenhua Zou, Shuo Li, Zhuotao Liu arxiv

Mobile Graphical User Interface (GUI) agents have demonstrated strong capabilities in automating complex smartphone tasks by leveraging multimodal large language models (MLLMs) and system-level control interfaces. Howeve…

Talking With Your Hands: Scaling Hand Gestures and Recognition With CNNs

2019-05-10 · Okan Köpüklü, Yao Rong, Gerhard Rigoll

The use of hand gestures provides a natural alternative to cumbersome interface devices for Human-Computer Interaction (HCI) systems. As the technology advances and communication between humans and machines becomes more …