Abstract

Physical-layer security uses the noise already present in the channel to keep a message secret without making any assumption about the eavesdropper's computing power. In Wyner's wiretap channel model, coset coding uses randomness to map the message to many codewords and protects against information leakage from an active eavesdropper. The secrecy provided by such codes is measured by their equivocation, which is the eavesdropper's remaining uncertainty about the message after she observes her channel. So computing equivocation is an important aspect of analyzing the wiretap channel model. Many channels of practical interest have no closed-form expression for equivocation, and exact calculation has been limited to a few specific channels. This dissertation studies the equivocation analysis of coset codes along two complementary directions. When a channel offers no closed-form solution, we estimate the information leakage with a neural network called the mutual information neural estimator (MINE). And for a discrete memoryless channel, we derive a general framework for the exact equivocation. The first study builds coset codes from short Reed-Muller codes and tests them over the air in a real indoor environment with software-defined radios. In this study, the secrecy is estimated using MINE, and short coset codes are shown to provide a secrecy advantage. The second study proposes a low-density parity-check (LDPC) code that injects artificial noise by flipping a chosen set of coded bits over a Gaussian wiretap channel. The third study moves from estimation to exact analysis. It examines coset coding over the Ghost Modulation wiretap channel, which is modeled as an asymmetric binary crossover erasure channel (ABCEC). Then it derives exact expressions for both the equivocation and the secrecy capacity. The final study generalizes the exact analysis to any discrete memoryless channel and verifies the framework against well-studied channels. Finally, it applies the developed framework to the asymmetric Z-channel with an efficient formulation that greatly reduces the computational cost.

Degree

PhD

College and Department

Ira A. Fulton College of Engineering; Electrical and Computer Engineering

Rights

https://lib.byu.edu/about/copyright/