A clustering-based differential privacy protection algorithm for weighted social networks.

Abstract

Weighted social networks play a crucial role in various fields such as social media analysis, healthcare, and recommendation systems. However, with their widespread application and privacy issues have become increasingly prominent, including concerns related to sensitive information leakage, individual behavior analysis, and privacy attacks. Despite traditional differential privacy protection algorithms being able to protect privacy for edges with sensitive information, directly adding noise to edge weights may result in excessive noise, thereby reducing data utility. To address these challenges, we proposed a privacy protection algorithm for weighted social networks called DCDP. The algorithm combines the density clustering algorithm OPTICS to partition the weighted social network into multiple sub-clusters and adds noise to different sub-clusters at random sampling frequencies. To enhance the balance of privacy protection, we designed a novel privacy parameter calculation method. Through theoretical derivation and experimentation, the DCDP algorithm demonstrated its capability to achieve differential privacy protection for weighted social networks while effectively maintaining data accuracy. Compared to traditional privacy protection algorithms, the DCDP algorithm reduced the average relative error by approximately 20% and increases the proportion of unchanged shortest paths by about 10%. In summary, we aimed to address privacy issues in weighted social networks, providing an effective method to protect user-sensitive information while ensuring the accuracy and utility of data analysis.