# Ensuring System-Level Protection against Eavesdropping Adversaries in Distributed Dynamical Systems

arXiv: https://arxiv.org/abs/2409.09539 · HTML: https://mlsanity.com/paper/2409.09539/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2024.

## Built on by (most cited first, 1 of 1)

- [On Model Protection in Federated Learning against Eavesdropping Attacks](https://mlsanity.com/paper/2504.02114/) (2025)

## Abstract (arXiv)

In this work, we address the objective of protecting the states of a distributed dynamical system from eavesdropping adversaries. We prove that state-of-the-art distributed algorithms, which rely on communicating the agents' states, are vulnerable in that the final states can be perfectly estimated by any adversary including those with arbitrarily small eavesdropping success probability. While existing literature typically adds an extra layer of protection, such as encryption or differential privacy techniques, we demonstrate the emergence of a fundamental protection quotient in distributed systems when innovation signals are communicated instead of the agents' states.