Compressed Sensing-Based Privacy Preserving in Labeled Dynamic Social Networks

Abstract

With the rapid development of social networks, privacy- preserving is an important issue. Nowadays, privacy-preserving mainly involves static social networks. In fact, social networks are dynamic. The sequential release of social networks will lead to privacy leakage. Many studies have employed <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-anonymity to protect users&#x0027; privacy, but because <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> affects data utility in social networks, a smaller <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> cannot provide enough privacy preserving, whereas a larger <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> will lead to a significant loss of data utility. This article proposes a new privacy-preserving approach based on compressed sensing (CS) to protect privacy in the labeled dynamic social networks. The scheme first uses the CS to compress the updated node information at each time point. Then, in the form of label grouping, the node link relationship is randomly deleted/changed to blur the node degrees to protect privacy. Finally, the reconstruction algorithm and Gaussian measurement matrix are combined to ensure operating efficiency while balancing data privacy and data utility. The simulation results show that our scheme retains data utility better than <inline-formula><tex-math notation="LaTeX">$l$</tex-math></inline-formula>-diversity and <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-anonymity. Furthermore, according to privacy analysis, the scheme can also protect link relationship privacy and label privacy at the same time, and prevent background knowledge attacks.