Privacy-Preserving Social Network Clustering Using Differential Privacy

In the contemporary landscape of online social networks, preserving users' privacy while applying clustering techniques is a pivotal concern. This paper explores the integration of differential privacy into social network clustering to strike a balance between privacy and clustering effectiveness. The principal objective is to safeguard users' personal information, and the study meticulously examines the trade-offs inherent in differential privacy parameters and their influence on clustering performance. This paper provides a comprehensive insight into the nuanced interplay between user privacy, data utility, and clustering effectiveness within the distinctive dynamics of social networks, offering a promising solution for the field.