Academic

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

arXiv:2602.13840v1 Announce Type: new 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

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Yuhan Cheng, Hancheng Ye, Hai Helen Li, Jingwei Sun, Yiran Chen · · 1 min read · 2 views
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arXiv:2602.13840v1 Announce Type: new 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.

Executive Summary

The article 'PrivAct: Internalizing Contextual Privacy Preservation via Multi-Agent Preference Training' introduces a novel framework designed to enhance privacy preservation in large language model (LLM) agents. The authors argue that existing methods, which rely on external interventions at inference time, are often brittle and scenario-specific, potentially expanding the privacy attack surface. PrivAct, in contrast, internalizes contextual privacy preservation directly into the models' generation behavior. By embedding privacy preferences into each agent, the framework aims to improve system-wide contextual integrity and achieve a better balance between privacy and helpfulness. Experimental results demonstrate significant improvements in privacy preservation across various LLM backbones and benchmarks, with a reduction in leakage rates by up to 12.32% while maintaining comparable helpfulness. The framework also shows robustness and generalization capabilities across diverse multi-agent topologies.

Key Points

  • ▸ PrivAct internalizes contextual privacy preservation into LLM agents' generation behavior.
  • ▸ The framework embeds privacy preferences into each agent to enhance system-wide contextual integrity.
  • ▸ Experiments show a reduction in privacy leakage rates by up to 12.32% while maintaining helpfulness.
  • ▸ PrivAct demonstrates zero-shot generalization and robustness across diverse multi-agent topologies.

Merits

Innovative Approach

PrivAct represents a significant advancement in the field by internalizing privacy preservation, moving away from external, inference-time interventions.

Empirical Validation

The framework is rigorously tested across multiple LLM backbones and benchmarks, providing strong empirical evidence of its effectiveness.

Generalization and Robustness

The framework's ability to generalize and maintain robustness across diverse multi-agent topologies is a notable strength.

Demerits

Complexity

The internalization of privacy preferences into each agent may introduce complexity in implementation and scalability.

Potential Overhead

Embedding privacy preferences into each agent could potentially increase computational overhead and resource requirements.

Limited Real-World Testing

While the experiments are comprehensive, real-world deployment and testing in varied and dynamic environments are not fully explored.

Expert Commentary

The article presents a compelling and innovative approach to addressing the critical issue of privacy preservation in LLM agents. By internalizing privacy preferences into the models' generation behavior, PrivAct offers a more robust and scalable solution compared to traditional external interventions. The empirical results are impressive, demonstrating significant improvements in privacy preservation while maintaining helpfulness. However, the complexity and potential overhead of embedding privacy preferences into each agent are notable limitations. Future research should focus on simplifying the implementation and reducing computational overhead. Additionally, real-world deployment and testing in varied and dynamic environments would provide further validation of the framework's effectiveness. Overall, PrivAct represents a significant step forward in the field of AI privacy and ethics, and its findings have important implications for both practical applications and policy development.

Recommendations

  • ✓ Further research should explore methods to simplify the implementation of PrivAct and reduce computational overhead.
  • ✓ Real-world deployment and testing in varied and dynamic environments should be conducted to validate the framework's effectiveness in practical scenarios.

Sources

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