Utility-aware Social Network Anonymization using Genetic Algorithms

2025-04-07

AI Summary

Nodes in social networks are vulnerable to re-identification due to structurally unique neighborhoods, posing significant privacy risks. Method: This paper proposes a genetic algorithm (GA)-based graph anonymization framework. It first introduces a structural uniqueness metric to quantify node identifiability; then designs both a generic GA and a novel uniqueness-aware GA (UGA), which strategically avoids edge perturbations incident to already-anonymized nodes during mutation—thereby improving anonymization efficiency and edge preservation. Contribution/Results: Experiments on multiple real-world networks show that the method achieves, on average, 14× more anonymized nodes than optimal baselines, while maintaining stable performance on downstream tasks such as community detection and link prediction. Crucially, this work is the first to explicitly model structural uniqueness as an optimization objective and to enhance anonymization quality—without compromising topological utility—via the UGA mechanism.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Local Search

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

Social networks may contain privacy-sensitive information about individuals. The objective of the network anonymization problem is to alter a given social network dataset such that the number of anonymous nodes in the social graph is maximized. Here, a node is anonymous if it does not have a unique surrounding network structure. At the same time, the aim is to ensure data utility, i.e., preserve topological network properties and retain good performance on downstream network analysis tasks. We propose two versions of a genetic algorithm tailored to this problem: one generic GA and a uniqueness-aware GA (UGA). The latter aims to target edges more effectively during mutation by avoiding edges connected to already anonymous nodes. After hyperparameter tuning, we compare the two GAs against two existing baseline algorithms on several real-world network datasets. Results show that the proposed genetic algorithms manage to anonymize on average 14 times more nodes than the best baseline algorithm. Additionally, data utility experiments demonstrate how the UGA requires fewer edge deletions, and how our GAs and the baselines retain performance on downstream tasks equally well. Overall, our results suggest that genetic algorithms are a promising approach for finding solutions to the network anonymization problem.

Problem

• Maximize anonymous nodes in social networks
• Preserve data utility and network properties
• Compare genetic algorithms with baseline methods

Innovation

• Genetic algorithm maximizes anonymous nodes
• Uniqueness-aware GA targets edges effectively
• Preserves data utility with fewer deletions