面向智能体系统的隐私偏好控制研究

View PDF HTML (experimental)

Abstract:In response to vast and widespread online tracking, regulators and technologists alike have worked to develop laws and tools that allow people to express their privacy preferences. These measures seek to offer users greater control over the sale and sharing of personal information, putting agency in their hands to protect their privacy. However, the web is undergoing a seismic shift, away from strictly \textit{user-driven} behavior towards \textit{agentic behavior}, where AI agents take autonomous actions on behalf of users. In this study, we ask: What should privacy preference controls look like in the agentic web and agentic systems more generally? To answer this question, we develop a taxonomy of enforceable privacy preferences for agentic systems. Our taxonomy is informed both by laws and empirical studies of existing privacy tools as well as an audit of 30 leading AI assistants and agent platforms. To explore how our taxonomy can be implemented, we design and develop eight open-source prototypes that mirror real-world agentic applications and demonstrate how major aspects of our taxonomy can be structurally integrated in agentic protocols, such as the Model Control Protocol and Agent2Agent protocol. Ultimately, our study seeks to provide the groundwork for ensuring end-user privacy and autonomy is not lost in the newly arriving agentic world.

Comments:	27 pages, 4 figures, 7 tables
Subjects:	Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
Cite as:	arXiv:2610.29929 [cs.CY]
	(or arXiv:2610.29929v1 [cs.CY] for this version)
	https://doi.org/10.48550/arXiv.2610.29929 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Shreya Kochar [view email]
[v1] Wed, 21 Oct 2026 16:03:02 UTC (510 KB)