发表机构
National Institute of Technology Calicut; Indian Institute of Technology Madras（卡利卡特国家理工学院; 印度理工学院马德拉斯分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于分布隐私的在线电子反对抗措施框架，通过vMF分布建模雷达效用并开发WDPCH-SU和WDPCH-DU算法，实现认知雷达决策的隐私掩蔽，在6G场景中具有应用价值。
AI 中文摘要
在本文中，我们提出了一种在线电子反对抗措施（ECCM）框架，旨在隐藏认知雷达（CR）在对抗性监视下运行的战略决策过程。我们在两种不同的决策范式下对CR进行建模：静态约束效用最大化行为和动态期望效用最大化行为。雷达的效用函数通过von Mises--Fisher（vMF）分布建模，其中分布参数构成需要防止对抗性推断的私有信息。我们采用分布隐私框架来隐藏该私有信息，并为认知掩蔽提供正式的分布隐私保证。在这项工作中，我们为静态约束效用最大化（WDPCH-SU）和动态期望效用最大化（WDPCH-DU）开发了认知隐藏算法。通过严格的数学分析，我们表明WDPCH-SU和WDPCH-DU均满足针对基于推断的对抗性攻击的$\epsilon$-分布隐私（$\epsilon$-DistP），并给出了隐私-性能权衡界限，将效用损失（静态设置中）和期望效用偏差（动态设置中）量化为$\epsilon$的函数。数值结果表明，在最大隐私保护下，与现有方法相比，WDPCH-SU在效用损失方面改善了约15%，而WDPCH-DU在不需要显式Fisher信息约束的情况下，实现了对抗性Fisher信息的更大减少，同时具有中等且解析有界的效用偏差。这些结果在许多6G通信场景中非常有前景，例如自动驾驶的网络切片和无人机群协调，在这些场景中，保持资源分配策略对隐私攻击的鲁棒性至关重要。
英文摘要
In this article, we propose an online electronic counter-countermeasure (ECCM) framework designed to conceal the strategic decision-making processes of a cognitive radar (CR) operating under adversarial surveillance. We model the CR under two distinct decision paradigms: a static constrained utility-maximizing behavior and a dynamic expected utility-maximizing behavior. The radar's utility function is modeled via a von Mises--Fisher (vMF) distribution, with the distributional parameter constituting the private information to be protected from adversarial inference. We adopt a distribution privacy framework to conceal this private information and provide formal distribution privacy guarantees for cognition masking. In this work, we develop cognition-hiding algorithms for both static constrained utility maximization (WDPCH-SU), and dynamic expected utility maximization (WDPCH-DU). Through rigorous mathematical analysis, we show that both WDPCH-SU and WDPCH-DU satisfy $ε$-distribution privacy ($ε$-DistP) against inference-based adversarial attacks and present the privacy--performance trade-off bounds, quantifying utility loss (in static setting) and expected utility deviation (in dynamic setting) as functions of $ε$. Numerical results show that WDPCH-SU gives about 15\% improvement in utility loss at maximum privacy compared to the existing methodology while WDPCH-DU achieves a greater reduction in adversarial Fisher information without requiring explicit Fisher information constraints, at a moderate, analytically bounded utility deviation. These results are highly promising in many 6G communication scenarios such as network slicing for automated driving and swarm UAV coordination, where it is essential to keep the resource allocation policy robust against privacy attacks.
