In this paper, a generalization of deep learning-aided joint source channel coding (Deep-JSCC) approach to secure communications is studied. We propose an end-to-end (E2E) learning-based approach for secure communication against multiple eavesdroppers over complex-valued fading channels. Both scenarios of colluding and non-colluding eavesdroppers are studied. For the colluding strategy, eavesdroppers share their logits to collaboratively infer private attributes based on ensemble learning method, while for the non-colluding setup they act alone. The goal is to prevent eavesdroppers from inferring private (sensitive) information about the transmitted images, while delivering the images to a legitimate receiver with minimum distortion. By generalizing the ideas of privacy funnel and wiretap channel coding, the trade-off between the image recovery at the legitimate node and the information leakage to the eavesdroppers is characterized. To solve this secrecy funnel framework, we implement deep neural networks (DNNs) to realize a data-driven secure communication scheme, without relying on a specific data distribution. Simulations over CIFAR-10 dataset verifies the secrecy-utility trade-off. Adversarial accuracy of eavesdroppers are also studied over Rayleigh fading, Nakagami-m, and AWGN channels to verify the generalization of the proposed scheme. Our experiments show that employing the proposed secure neural encoding can decrease the adversarial accuracy by 28%.

翻译：本文研究了深度学习辅助联合信源信道编码（Deep-JSCC）在安全通信中的推广方法。我们提出了一种基于端到端（E2E）学习的方案，用于在复值衰落信道上实现针对多窃听者的安全通信。研究同时考虑了合谋与非合谋两种窃听场景。在合谋策略中，窃听者共享其逻辑值，基于集成学习方法协同推断私有属性；而在非合谋设置中，各窃听者独立行动。目标是在将图像以最小失真传输至合法接收者的同时，阻止窃听者推断传输图像中的私有（敏感）信息。通过推广隐私漏斗与窃听信道编码的思想，本文刻画了合法节点图像恢复质量与窃听者信息泄露之间的权衡。为解决该安全漏斗框架，我们采用深度神经网络（DNNs）实现数据驱动的安全通信方案，无需依赖特定数据分布。在CIFAR-10数据集上的仿真验证了安全性与效用性的权衡关系。此外，通过瑞利衰落、Nakagami-m及加性高斯白噪声（AWGN）信道下的实验，验证了所提方案对窃听者对抗精度的泛化能力。实验表明，采用所提出的安全神经编码可使对抗精度降低28%。