Guarding Your Social Circle: Strategies to Protect Key Connections and Edge Importance

Abstract

With the growing use of social networks and the consequent rise in the sharing of personal information online, privacy has become a major concern, leading to an increased demand for efficient anonymization techniques. This research proposes innovative methods for hiding information in weighted social network graphs. We provide a topology-modification technique for precisely hiding important nodes in the user network. We also present a differential privacy-based method to safeguard edge weights while taking weight topology correlations into account. With accuracy rates of 87.04% and 94.73%, respectively, our approaches are highly effective in connections to significant nodes and concealing edge weights, respectively. Our methods, which safeguard data integrity to a larger extent than earlier techniques, alter the original graph as little as possible (only 12% of the original edges are changed), as measured by well-known graph metrics. Experiment results on the extracted Instagram posts show that our solutions outperform current approaches in terms of privacy and preserved usefulness.