智能体互联网中隐私增强互操作框架设计与优化

居彬; 王方圆; 罗童; 张羽

doi:10.11959/j.issn.1000-0801.DXKX260297

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智能体互联网中隐私增强互操作框架设计与优化

更新时间：2026-07-21

- 智能体互联网中隐私增强互操作框架设计与优化
- Telecommunications Science (2026)
- 作者机构：
  
  1.江苏省公用信息有限公司，江苏南京 210003
  2.南京理工大学，江苏南京 210094
  3.恒安嘉新（北京）科技股份公司，北京 100086
- 作者简介：
- 基金信息：
  
  Jiangsu Provincial Frontier Technology R&D Program(BF2025004)
- DOI：10.11959/j.issn.1000-0801.DXKX260297
  CLC： TP393.08;TN915
- Received：11 May 2026，
  
  Revised：2026-07-20，
- 稿件说明：
移动端阅览
居彬, 王方圆, 罗童, et al. 智能体互联网中隐私增强互操作框架设计与优化[J/OL]. Telecommunications Science, 2026.
DOI：

居彬, 王方圆, 罗童, et al. 智能体互联网中隐私增强互操作框架设计与优化[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260297.

摘要

随着物联网与人工智能技术的深度融合，由海量智能体协同互联构成的智能体互联网（Internet of Agents）正逐步迈向深度协同。然而，智能体在互操作过程中需频繁交换状态信息与决策知识，面临严峻的隐私泄露风险。现有研究大多将隐私保护视为性能优化的附属目标，缺乏系统性的内生安全机制。为此，本文提出一种隐私增强的互操作框架。该框架首先通过数字孪生技术为每个物理智能体构建虚拟映射，在孪生空间中形成隔离的交互沙盘；进而设计基于本地差分隐私的条件生成式对抗网络（LDP-CGAN），将加噪后的本地观测在孪生空间中复原为富含语义的虚拟数据，从源头切断原始数据流；最后提出联邦多智能体深度确定性策略梯度算法（FC-MADDPG），利用虚拟数据进行协同策略训练，并引入互操作性得分指导联邦聚合，实现通信、计算与隐私资源的联合优化。在MedMNIST医疗推理任务场景中的实验表明，与传统多智能体强化学习算法相比，本框架在引入强隐私保护的同时，任务平均时延与能耗仅分别增加5.1%与6.8%；模型逆向攻击的隐私泄露率从84.5%降至13.2%。实验结果验证了本框架在保障隐私安全的同时能够有效维持系统协同效能。

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