多智能体网络中用于推理隐私保护的信任感知自适应信息披露
Trust-Aware Adaptive Disclosure for Inference Privacy Preservation in Multi-Agent Networks
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中文总结 AI 辅助
该研究针对多智能体系统的目标推理攻击，提出Trust-Aware Privacy Control框架，通过依赖信任的随机策略权衡一致性性能与隐私保护，实验显示其能降低对手推理准确率且保持一致效用。
中文摘要 AI 辅助
基于智能体的系统正越来越多地部署在医疗管理系统、智能电网等信息关键系统中。本文考虑一个多智能体系统，其中每个智能体都有需要对观察的对手隐藏的潜在目标，更具体地说，本文研究网络化多智能体系统在目标推理攻击下的隐私保护一致性问题。我们提出了Trust-Aware Privacy Control（信任感知隐私控制）框架，该框架基于智能体间的动态信任关系自适应调整信息披露，采用依赖信任的随机策略控制信息释放，从而在一致性性能与隐私保护之间实现权衡。实验表明，与代表性基线方法相比，该方法降低了对手的目标推理准确率，同时保持了具有竞争力的一致性效用，凸显了信任感知机制在多智能体系统智能体隐私保护中的有效性。
英文摘要
Agent based systems are increasingly deployed in information critical systems including healthcare management systems, and smart grids. In this paper, we consider a multi-agent system where each agent has a latent goal that needs to be kept hidden from observing adversaries. More specifically, this paper studies privacy-preserving consensus in networked multi-agent systems under goal inference attacks. We propose a Trust-Aware Privacy Control framework that adapts message disclosure based on the dynamic trust relationships between agents. The proposed method controls information release using a trust-dependent stochastic policy. This enables a tradeoff between consensus performance and privacy preservation. Experiments demonstrate that the proposed method reduces adversarial goal inference accuracy compared to representative baselines, while maintaining competitive consensus utility, thereby highlighting the effectiveness of trust-aware mechanisms in privacy preservation of the agents in multi-agent systems.
发表机构
- Indian Statistical Institute（印度统计研究所）
- KTH Royal Institute of Technology（皇家理工学院）
机构由 AI 辅助整理，请以论文原文为准。
