PrivAct: Internalizing Contextual Privacy Preservation via Multi-Agent Preference Training

Yuhan Cheng, Hancheng Ye, Hai Helen Li, Jingwei Sun, Yiran Chen
Proceedings of the 43rd International Conference on Machine Learning, PMLR 306:19079-19098, 2026.

Abstract

Large language model (LLM) agents are increasingly deployed in personalized tasks involving sensitive, context-dependent information, where privacy violations may arise in agents’ action due to the implicitness of contextual privacy. Existing approaches rely on external, inference-time interventions which are brittle, scenario-specific, and may expand the privacy attack surface. We propose PrivAct, a contextual privacy-aware multi-agent learning framework that internalizes contextual privacy preservation directly into models’ generation behavior for privacy-compliant agentic actions. By embedding privacy preferences into each agent, PrivAct enhances system-wide contextual integrity while achieving a more favorable privacy-helpfulness tradeoff. Experiments across multiple LLM backbones and benchmarks demonstrate consistent improvements in contextual privacy preservation, reducing leakage rates by up to 12.32% while maintaining comparable helpfulness, as well as zero-shot generalization and robustness across diverse multi-agent topologies. Code is available at https://github.com/chengyh23/PrivAct.

Cite this Paper

BibTeX
@InProceedings{pmlr-v306-cheng26o, title = {{P}riv{A}ct: Internalizing Contextual Privacy Preservation via Multi-Agent Preference Training}, author = {Cheng, Yuhan and Ye, Hancheng and Li, Hai Helen and Sun, Jingwei and Chen, Yiran}, booktitle = {Proceedings of the 43rd International Conference on Machine Learning}, pages = {19079--19098}, year = {2026}, editor = {Zhang, Tong and Dudik, Miroslav and Jaggi, Martin and Agarwal, Alekh and Li, Sharon and Schuurmans, Dale and Zhu, Jerry and Berkenkamp, Felix and Dong, Hanze and Bietti, Alberto}, volume = {306}, series = {Proceedings of Machine Learning Research}, month = {06--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v306/main/assets/cheng26o/cheng26o.pdf}, url = {https://proceedings.mlr.press/v306/cheng26o.html}, abstract = {Large language model (LLM) agents are increasingly deployed in personalized tasks involving sensitive, context-dependent information, where privacy violations may arise in agents’ action due to the implicitness of contextual privacy. Existing approaches rely on external, inference-time interventions which are brittle, scenario-specific, and may expand the privacy attack surface. We propose PrivAct, a contextual privacy-aware multi-agent learning framework that internalizes contextual privacy preservation directly into models’ generation behavior for privacy-compliant agentic actions. By embedding privacy preferences into each agent, PrivAct enhances system-wide contextual integrity while achieving a more favorable privacy-helpfulness tradeoff. Experiments across multiple LLM backbones and benchmarks demonstrate consistent improvements in contextual privacy preservation, reducing leakage rates by up to 12.32% while maintaining comparable helpfulness, as well as zero-shot generalization and robustness across diverse multi-agent topologies. Code is available at https://github.com/chengyh23/PrivAct.} }
Endnote
%0 Conference Paper %T PrivAct: Internalizing Contextual Privacy Preservation via Multi-Agent Preference Training %A Yuhan Cheng %A Hancheng Ye %A Hai Helen Li %A Jingwei Sun %A Yiran Chen %B Proceedings of the 43rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2026 %E Tong Zhang %E Miroslav Dudik %E Martin Jaggi %E Alekh Agarwal %E Sharon Li %E Dale Schuurmans %E Jerry Zhu %E Felix Berkenkamp %E Hanze Dong %E Alberto Bietti %F pmlr-v306-cheng26o %I PMLR %P 19079--19098 %U https://proceedings.mlr.press/v306/cheng26o.html %V 306 %X Large language model (LLM) agents are increasingly deployed in personalized tasks involving sensitive, context-dependent information, where privacy violations may arise in agents’ action due to the implicitness of contextual privacy. Existing approaches rely on external, inference-time interventions which are brittle, scenario-specific, and may expand the privacy attack surface. We propose PrivAct, a contextual privacy-aware multi-agent learning framework that internalizes contextual privacy preservation directly into models’ generation behavior for privacy-compliant agentic actions. By embedding privacy preferences into each agent, PrivAct enhances system-wide contextual integrity while achieving a more favorable privacy-helpfulness tradeoff. Experiments across multiple LLM backbones and benchmarks demonstrate consistent improvements in contextual privacy preservation, reducing leakage rates by up to 12.32% while maintaining comparable helpfulness, as well as zero-shot generalization and robustness across diverse multi-agent topologies. Code is available at https://github.com/chengyh23/PrivAct.
APA
Cheng, Y., Ye, H., Li, H.H., Sun, J. & Chen, Y.. (2026). PrivAct: Internalizing Contextual Privacy Preservation via Multi-Agent Preference Training. Proceedings of the 43rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 306:19079-19098 Available from https://proceedings.mlr.press/v306/cheng26o.html.

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