Privacy Preservation in Online Social Networks Using Multiple-Graph-Properties-Based Clustering to Ensure k-Anonymity, l-Diversity, and t-Closeness

As per recent progress, online social network (OSN) users have grown tremendously worldwide, especially in the wake of COVID-19 pandemic. Today, OSNs become a core part many people’s daily lifestyles. Therefore, increasing dependency on encourages privacy requirements to protect from malicious sources. contain sensitive information about each end user that intruders may try leak for commercial or non-commercial purposes. ensuring different levels is vital requirement OSNs. Various preservation methods been introduced recently at and levels, but k-anonymity higher model such as l-diversity t-closeness still research challenge. This study proposes novel method effectively anonymizes using multiple-graph-properties-based clustering. The clustering introduces goal achieving edge, node, attributes OSN graph. approach ensure k-anonymity, l-diversity, cluster proposed model. We first design data normalization algorithm preprocess enhance quality raw data. Then, we divide into clusters multiple graph properties satisfy k-anonymization. Furthermore, improved k-anonymization by one-pass anonymization address requirements. evaluate performance with state-of-the-art “Yelp real-world dataset”. ensures high-level compared metrics degree, loss, execution time.