Helper-Assisted Coding for Gaussian Wiretap Channels: Deep Learning Meets PhySec

TL;DR

This paper introduces a novel deep learning-based coding scheme for Gaussian wiretap channels with helpers, achieving improved secrecy and practicality over existing methods through explicit, short blocklength codes.

Contribution

It presents the first explicit deep learning-based code design for helper-assisted Gaussian wiretap channels, demonstrating practical benefits and improved security compared to prior non-constructive schemes.

Findings

Deep learning codes outperform existing codes in reducing information leakage.

The autoencoder architecture enables efficient and practical code implementation.

The approach extends to multiple access wiretap channels with helpers.

Abstract

Consider the Gaussian wiretap channel, where a transmitter wishes to send a confidential message to a legitimate receiver in the presence of an eavesdropper. It is well known that if the eavesdropper experiences less channel noise than the legitimate receiver, then it is impossible for the transmitter to achieve positive secrecy rates. A known solution to this issue consists in involving a second transmitter, referred to as a helper, to help the first transmitter to achieve security. While such a solution has been studied for the asymptotic blocklength regime and via non-constructive coding schemes, in this paper, for the first time, we design explicit and short blocklength codes using deep learning and cryptographic tools to demonstrate the benefit and practicality of cooperation between two transmitters over the wiretap channel. Specifically, our proposed codes show strict improvement in terms of information leakage compared to existing codes that do not consider a helper. Our code design approach relies on a reliability layer, implemented with an autoencoder architecture based on the successive interference cancellation method, and a security layer implemented with universal hash functions. We also propose an alternative autoencoder architecture that significantly reduces training time by allowing the decoders to independently estimate messages without successively canceling interference by the receiver during training. Additionally, we show that our code design is also applicable to the multiple access wiretap channel with helpers, where two transmitters send confidential messages to the legitimate receiver.