Concealing-Restoring System for Physical Layer Data Based on Stochastic Filtering Theory

TL;DR

This paper introduces a concealing-restoring system for physical layer data that uses stochastic filtering to hide data with noise and restore it accurately, enhancing security through nonlinearization.

Contribution

It details the nonlinearization of a stochastic filtering-based CRS, improving data security at the physical layer.

Findings

Effective data concealment with noise disturbance.

Accurate data restoration using stochastic filtering.

Enhanced security through nonlinearization.

Abstract

We propose a concealing-restoring system (CRS) for data on physical layer of the OSI reference model. CRS conceals those data by disturbing them with some random noises, and restores the data from the concealed ones to the original ones by using the noise elimination based on a proper stochastic filtering theory. Although we introduced the outline of the almost linear version of CRS in our previous work [Fujii & Hirokawa, Math. Industry, Springer (2020)], we explain its details, and study its nonlinearization to improve the security of CRS in this paper.