arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2408.02095cs.ITeess.SPmath.IT
收藏

安全语义通信:物理层安全视角

Secure Semantic Communications: From Perspective of Physical Layer Security

Yongkang Li, Zheng Shi, Han Hu, Yaru Fu, Hong Wang, Hongjiang Lei

AI总结:

针对语义通信窃听可能泄露用户意图的问题,本文提出物理层安全驱动的DeepSSC系统,采用两阶段DNN训练并定义S-BLEU指标,在高信噪比下显著增强语义安全。

摘要:

语义通信被设想为一种超越Shannon范式的潜在技术。不同于提供比特级安全的现代通信,语义通信遭到窃听会带来重大风险,可能暴露合法用户的意图。为应对这一挑战,本文利用物理层安全,开发了一种新型深度神经网络(DNN)使能的安全语义通信(DeepSSC)系统。为平衡安全性与可靠性之间的折中,设计了一种用于DNN的两阶段训练方法。具体而言,阶段I旨在实现合法用户的语义恢复,而阶段II试图最小化向窃听者泄露的语义信息。DeepSSC在阶段I和阶段II的损失函数分别依据Shannon容量和安全信道容量设计,并通过变分推断进行近似。此外,本文定义了安全双语评估替补(S-BLEU)指标,以评估语义通信的安全性。最后,仿真结果表明,尽管可靠性略有下降,DeepSSC尤其在高信噪比区域显著提升了语义安全性。

英文摘要:

Semantic communications have been envisioned as a potential technique that goes beyond Shannon paradigm. Unlike modern communications that provide bit-level security, the eaves-dropping of semantic communications poses a significant risk of potentially exposing intention of legitimate user. To address this challenge, a novel deep neural network (DNN) enabled secure semantic communication (DeepSSC) system is developed by capitalizing on physical layer security. To balance the tradeoff between security and reliability, a two-phase training method for DNNs is devised. Particularly, Phase I aims at semantic recovery of legitimate user, while Phase II attempts to minimize the leakage of semantic information to eavesdroppers. The loss functions of DeepSSC in Phases I and II are respectively designed according to Shannon capacity and secure channel capacity, which are approximated with variational inference. Moreover, we define the metric of secure bilingual evaluation understudy (S-BLEU) to assess the security of semantic communications. Finally, simulation results demonstrate that DeepSSC achieves a significant boost to semantic security particularly in high signal-to-noise ratio regime, despite a minor degradation of reliability.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑