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Computer Science > Information Theory

arXiv:2212.04288 (cs)
[Submitted on 8 Dec 2022 (v1) , last revised 22 Dec 2022 (this version, v2)]

Title: Secure Over-the-Air Computation using Zero-Forced Artificial Noise

Title: 使用零强迫人工噪声的空中安全计算

Authors:Luis Maßny, Antonia Wachter-Zeh
Abstract: Over-the-air computation has the potential to increase the communication-efficiency of data-dependent distributed wireless systems, but is vulnerable to eavesdropping. We consider over-the-air computation over block-fading additive white Gaussian noise channels in the presence of a passive eavesdropper. The goal is to design a secure over-the-air computation scheme. We propose a scheme that achieves MSE-security against the eavesdropper by employing zero-forced artificial noise, while keeping the distortion at the legitimate receiver small. In contrast to former approaches, the security does not depend on external helper nodes to jam the eavesdropper's received signal. We thoroughly design the system parameters of the scheme, propose an artificial noise design that harnesses unused transmit power for security, and give an explicit construction rule. Our design approach is applicable in both cases, if the eavesdropper's channel coefficients are known and if they are unknown in the signal design. Simulations demonstrate the performance, and show that our noise design outperforms other methods.
Abstract: 基于空中计算有望提高数据依赖型分布式无线系统的通信效率,但容易受到窃听的威胁。 我们考虑在存在被动窃听者的情况下,在块衰落加性高斯白噪声信道上进行空中计算。 目标是设计一种安全的空中计算方案。 我们提出了一种方案,通过采用零强制人工噪声来实现对窃听者的均方误差安全性,同时保持合法接收端的失真较小。 与之前的方法不同,安全性不依赖于外部中继节点来干扰窃听者接收到的信号。 我们全面设计了该方案的系统参数,提出了一种利用未使用的发射功率进行安全的人工噪声设计,并给出了明确的构造规则。 我们的设计方法在两种情况下都适用,即当窃听者的信道系数已知和未知时。 仿真结果展示了性能,并表明我们的噪声设计优于其他方法。
Subjects: Information Theory (cs.IT) ; Cryptography and Security (cs.CR); Machine Learning (cs.LG)
Cite as: arXiv:2212.04288 [cs.IT]
  (or arXiv:2212.04288v2 [cs.IT] for this version)
  https://doi.org/10.48550/arXiv.2212.04288
arXiv-issued DOI via DataCite

Submission history

From: Luis Maßny [view email]
[v1] Thu, 8 Dec 2022 14:30:59 UTC (39 KB)
[v2] Thu, 22 Dec 2022 16:13:33 UTC (39 KB)
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