Rethinking the Intercept Probability of Random Linear Network Coding

TL;DR

This paper analyzes the probability of an eavesdropper intercepting enough coded packets in random linear network coding, providing closed-form expressions and optimizing for secrecy under delay and reliability constraints.

Contribution

It introduces a new analysis of intercept probability in network coding and proposes an optimization model to enhance secrecy with feedback mechanisms.

Findings

Closed-form intercept probability expressions derived.

Feedback improves secrecy performance.

Optimization reduces intercept probability under constraints.

Abstract

This letter considers a network comprising a transmitter, which employs random linear network coding to encode a message, a legitimate receiver, which can recover the message if it gathers a sufficient number of linearly independent coded packets, and an eavesdropper. Closed-form expressions for the probability of the eavesdropper intercepting enough coded packets to recover the message are derived. Transmission with and without feedback is studied. Furthermore, an optimization model that minimizes the intercept probability under delay and reliability constraints is presented. Results validate the proposed analysis and quantify the secrecy gain offered by a feedback link from the legitimate receiver.