PrivWeb: Unobtrusive and Content-aware Privacy Protection For Web Agents
Year: 2026
Type: conference-paper
Abstract: While web agents gained popularity by automating web interactions, their requirement for interface access introduces privacy risks that are understudied, particularly from users’ perspective. Through a formative study (N=15), we found that users frequently misunderstand agent data practices, and desire unobtrusive, transparent data management. To achieve this, we developed PrivWeb, a trusted add-on on web agents that utilizes a localized LLM to anonymize private information on interfaces based on user preferences. It employs a tiered delegation to balance automation and intrusiveness, using ambient notifications for low-sensitivity data and enforces a mandatory pause for high-sensitivity data. The user study (N=14) across travel, information retrieval, shopping, and entertainment tasks showed that PrivWeb enhances perceived privacy protection and trust compared to transparency-only baselines, without increasing cognitive load. Crucially, we identified user delegation strategies: they prefer to manually execute sensitive steps for high-sensitivity data, while granting agent access to low-sensitivity data.
Authors: Shuning Zhang, Yutong Jiang, Rongjun Ma, Yuting Yang, Mingyao Xu, Zhixin Huang, Xin Yi, Hewu Li
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4415089257