Privacy Limits in Power-Law Bipartite Networks under Active Fingerprinting Attacks

TL;DR

This paper investigates the privacy limits in power-law bipartite networks under active fingerprinting attacks, proposing a new attack method and analyzing its success conditions through simulations.

Contribution

It introduces the A-ITS attack strategy and derives conditions for its success, advancing understanding of privacy vulnerabilities in power-law bipartite networks.

Findings

A-ITS outperforms existing attack strategies in simulations.

Node degree distribution follows a power-law with arbitrary parameter > 2.

Sufficient conditions for attack success are derived.

Abstract

This work considers the fundamental privacy limits under active fingerprinting attacks in power-law bipartite networks. The scenario arises naturally in social network analysis, tracking user mobility in wireless networks, and forensics applications, among others. A stochastic growing network generation model -- called the popularity-based model -- is investigated, where the bipartite network is generated iteratively, and in each iteration vertices attract new edges based on their assigned popularity values. It is shown that using the appropriate choice of initial popularity values, the node degree distribution follows a power-law distribution with arbitrary parameter $\alpha>2$, i.e. fraction of nodes with degree $d$ is proportional to $d^{-\alpha}$. An active fingerprinting deanonymization attack strategy called the augmented information threshold attack strategy (A-ITS) is proposed which uses the attacker's knowledge of the node degree distribution along with the concept of information values for deanonymization. Sufficient conditions for the success of the A-ITS, based on network parameters, are derived. It is shown through simulations that the proposed attack significantly outperforms the state-of-the-art attack strategies.