Statistical privacy-preserving message dissemination for peer-to-peer networks

TL;DR

This paper introduces a statistical model for privacy-preserving message dissemination in peer-to-peer networks, leveraging normal distribution estimators to optimize forwarding probabilities and enhance privacy.

Contribution

It transforms a contact network protocol into a P2P network protocol and models shortest path distributions to improve privacy in message broadcasting.

Findings

Shortest path distribution approximates a normal distribution.

Model parameters depend logarithmically on network size.

Optimal forwarding probabilities are derived for privacy enhancement.

Abstract

Concerns for the privacy of communication is widely discussed in research and overall society. For the public financial infrastructure of blockchains, this discussion encompasses the privacy of transaction data and its broadcasting throughout the network. To tackle this problem, we transform a discrete-time protocol for contact networks over infinite trees into a computer network protocol for peer-to-peer networks. Peer-to-peer networks are modeled as organically growing graphs. We show that the distribution of shortest paths in such a network can be modeled using a normal distribution $\mathcal{N}(\mu,\sigma^2).$ We determine statistical estimators for $\mu,\sigma$ via multivariate models. The model behaves logarithmic over the number of nodes n and proportional to an inverse exponential over the number of added edges k. These results facilitate the computation of optimal forwarding probabilities during the dissemination phase for optimal privacy in a limited information environment.