Social Network Privacy Protection Based on Differential Privacy Technology and Community Discovery Algorithm

Abstract

The high aggregation of user relationship and behavioral data in social networks continues to aggravate privacy leaks. How to strike a balance between privacy protection and data availability has become a research hotspot. To collaboratively optimize user information security and community structure identification, this study proposes a social network privacy protection model that integrates differential privacy technology and community discovery algorithms. First, a differential privacy noise injection mechanism is constructed to perturb node data and combine it with blockchain storage to ensure that the data cannot be tampered with. Then, a community division strategy based on information entropy and mutual information is introduced to achieve high-precision community identification through modularity optimization. The accuracy of the proposed model reached 98.1% when the data set size was 800, which was about 3.4% and 9% higher than that of other models, respectively. The root mean square error was 8.2, which was about 20% lower than that of the traditional model. The convergence speed was increased to 380 iterations, which was about 15% faster than that of the comparison algorithm. The privacy protection strength and scalability scores reached 9.3 and 9.5, respectively. The simulation test results showed that, under different data types, the accuracy of the model grew from 0.87 to 0.98, and the F1 value grew from 0.84 to 0.95. The integration of differential privacy and community discovery effectively improves the privacy protection strength and structural analysis accuracy of social networks, providing a highly feasible solution for multi-scenario social data security analysis.