Secure Deep-JSCC Against Multiple Eavesdroppers

TL;DR

This paper introduces a deep learning-based secure joint source-channel coding scheme that protects transmitted images from multiple eavesdroppers, balancing image quality at the receiver with privacy against various channel conditions.

Contribution

It presents a novel end-to-end neural network approach for secure communication that handles multiple eavesdroppers and generalizes privacy protection over complex channels.

Findings

Reduces eavesdroppers' adversarial accuracy by 28%.

Verifies effectiveness over Rayleigh, Nakagami-m, and AWGN channels.

Demonstrates a privacy-utility trade-off in secure image transmission.

Abstract

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%.