Community hiding: Completely escape from community detection

Abstract

Community detection algorithms play an important role in revealing and analyzing complex network structures. However, when these algorithms are put into practice, privacy concerns often arise, especially when community nodes wish to safeguard their identities from exposure. This interaction between network analysis and privacy issues leads to what we refer to as the “community hiding problem.” In the context of concealing specific target communities, popular methods primarily focus on preventing community detection algorithms from accurately identifying the entire original community, resulting in partial concealment. This study formally addresses the challenge of achieving complete concealment of target communities. We introduce key metrics such as escape scores, dispersion scores, and hiding scores to precisely define the problem. Additionally, we design a metric, denoted as M-value, to evaluate the effectiveness of concealing target communities from multiple perspectives. To tackle this challenge, we employ a genetic algorithm that leverages previous knowledge to maximize the concealment of the target community while minimizing changes to link connections. We validate our approach through extensive experiments with various real-world datasets, demonstrating that our algorithm outperforms several state-of-the-art baseline algorithms across multiple metrics. Visual representations of our method confirm its ability to effectively hide target communities while minimally affecting the broader network's community structure, strengthening the inherent effectiveness of our community hiding approach. Furthermore, we assess the adaptability of our modified network with different community detection algorithms, consistently demonstrating effective concealment even when these algorithms are extended. This emphasizes the robustness and generality of our proposed approach in various algorithmic scenarios.