Differentially Private Bipartite Consensus over Signed Networks with Time-Varying Noises

TL;DR

This paper presents a differentially private bipartite consensus algorithm for signed networks that employs time-varying noise to balance privacy and convergence, with proven convergence guarantees and practical effectiveness.

Contribution

It introduces a novel privacy-preserving algorithm using time-varying noise variances, allowing for flexible privacy levels while ensuring convergence in signed networks.

Findings

The algorithm converges in mean-square and almost-surely despite increasing privacy noise.

It can achieve asymptotically unbiased bipartite consensus with desired accuracy.

Trade-offs between convergence rate and privacy level are characterized.

Abstract

This paper investigates the differentially private bipartite consensus algorithm over signed networks. The proposed algorithm protects each agent's sensitive information by adding noise with time-varying variances to the cooperative-competitive interactive information. In order to achieve privacy protection, the variance of the added noise is allowed to be increased, and substantially different from the existing works. In addition, the variance of the added noise can be either decaying or constant. By using time-varying step-sizes based on the stochastic approximation method, we show that the algorithm converges in mean-square and almost-surely even with an increasing privacy noise. We further develop a method to design the step-size and the noise parameter, affording the algorithm to achieve asymptotically unbiased bipartite consensus with the desired accuracy and the predefined differential privacy level. Moreover, we give the mean-square and almost-sure convergence rate of the algorithm, and the privacy level with different forms of the privacy noises. We also reveal the algorithm's trade-off between the convergence rate and the privacy level. Finally, a numerical example verifies the theoretical results and demonstrates the algorithm's superiority against existing methods.