학술논문

Wiretap Code Design by Neural Network Autoencoders
Document Type
Periodical
Source
IEEE Transactions on Information Forensics and Security IEEE Trans.Inform.Forensic Secur. Information Forensics and Security, IEEE Transactions on. 15:3374-3386 2020
Subject
Signal Processing and Analysis
Computing and Processing
Communication, Networking and Broadcast Technologies
Reliability
Computational modeling
Decoding
Artificial neural networks
Mutual information
Neurons
Physical layer security
wiretap codes
deep learning
autoencoders
Language
ISSN
1556-6013
1556-6021
Abstract
In industrial machine type communications, an increasing number of wireless devices communicate under reliability, latency, and confidentiality constraints, simultaneously. From information theory, it is known that wiretap codes can asymptotically achieve reliability (vanishing block error rate (BLER) at the legitimate receiver Bob) while also achieving secrecy (vanishing information leakage (IL) to an eavesdropper Eve). However, under finite block length, there exists a tradeoff between the BLER at Bob and the IL at Eve. In this work, we propose a flexible wiretap code design for degraded Gaussian wiretap channels under finite block length, which can change the operating point on the Pareto boundary of the tradeoff between BLER and IL given specific code parameters. To attain this goal, we formulate a multi-objective programming problem, which takes the BLER at Bob and the IL at Eve into account. During training, we approximate the BLER by the mean square error and the IL by schemes based on Jensen’s inequality and the Taylor expansion and then solve the optimization problem by neural network autoencoders. Simulation results show that the proposed scheme can find codes outperforming polar wiretap codes (PWC) with respect to both BLER and IL simultaneously. We show that the codes found by the autoencoders could be implemented with real modulation schemes with only small losses in performance.