In recent years, the proliferation of social networks has been remarkable, providing a rich source for data mining endeavors. However, a significant challenge lies in safeguarding the privacy of individuals while sharing these databases publicly. Current approaches, such as K-anonymity, L-diversity, and T-closeness, are commonly employed for data anonymization in social networks. However, these techniques entail considerable information loss due to random alterations in the graph-based datasets. To address these limitations, this paper introduces a new anonymization technique called KMFC-GWO, which combines K-Member Fuzzy Clustering with Grey Wolf Optimizer. This integrated method is designed to strengthen the anonymized graph against a range of threats, including identity, attribute, link disclosure, and similarity attacks, while significantly reducing information loss. Within the KMFC-GWO framework, K-member fuzzy c-means clustering is utilized to create well-balanced clusters, each meeting the K-anonymity requirement. Subsequently, the Grey Wolf Optimizer is applied to optimize cluster formation and effectively anonymize the social network graph. The objective function is carefully crafted to minimize both clustering error and information loss, while ensuring adherence to predefined anonymity criteria. Experimentation on three major graph-based social networks extracted from Facebook, Twitter, and YouTube validates the effectiveness of the KMFC-GWO approach. Results demonstrate its ability to significantly reduce information loss in published graph data, while concurrently satisfying requirements for K-anonymity, L-diversity, and T-closeness.
Memarian et al. (2026) studied this question.