Unnoticeable Community Deception via Multi-objective Optimization

2025-09-01

AI Summary

Existing community deception methods inadequately evaluate plausibility and attack imperceptibility, compromising privacy protection in graph data. Method: This paper proposes a privacy-preserving multi-objective community deception framework featuring: (1) a novel deception metric jointly optimizing modularity reduction and structural perturbation minimization; (2) a candidate node selection mechanism integrating degree and community preference; and (3) a multi-objective optimization algorithm that jointly constrains attack budget, degree deviation, and community deviation. Results: Experiments on three benchmark datasets demonstrate that the proposed method significantly degrades community detection accuracy while maintaining extremely low structural detectability. It outperforms state-of-the-art approaches in overall performance, substantially enhancing the concealment of individual privacy in graph-structured data.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Privacy
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Community detection in graphs is crucial for understanding the organization of nodes into densely connected clusters. While numerous strategies have been developed to identify these clusters, the success of community detection can lead to privacy and information security concerns, as individuals may not want their personal information exposed. To address this, community deception methods have been proposed to reduce the effectiveness of detection algorithms. Nevertheless, several limitations, such as the rationality of evaluation metrics and the unnoticeability of attacks, have been ignored in current deception methods. Therefore, in this work, we first investigate the limitations of the widely used deception metric, i.e., the decrease of modularity, through empirical studies. Then, we propose a new deception metric, and combine this new metric together with the attack budget to model the unnoticeable community deception task as a multi-objective optimization problem. To further improve the deception performance, we propose two variant methods by incorporating the degree-biased and community-biased candidate node selection mechanisms. Extensive experiments on three benchmark datasets demonstrate the superiority of the proposed community deception strategies.

Problem

• Addressing privacy concerns in community detection algorithms
• Improving unnoticeability in community deception attacks
• Optimizing deception metrics with multi-objective approaches

Innovation

• Multi-objective optimization for unnoticeable deception
• New deception metric combined with attack budget
• Degree-biased and community-biased node selection