Flexible k-anonymity Scheme Suitable for Different Scenarios in Social Networks

Abstract

Social networks not only help expand interpersonal interactions, enable data analysis, and implement intelligent recommendations, but also can deeply examine social structures and dynamic changes between individuals, making them an indispensable part of contemporary society. However, malicious entities pose a significant threat to user identity and relationship information within social networks, raising concerns about privacy and security issues. Although existing k-anonymity schemes provide certain privacy protection, they lack the flexibility to adjust the intensity of privacy protection according to specific scenarios and user preferences, thus seriously compromising the utility of anonymized data. Based on the isomorphic algorithm, this paper proposes a new structural anonymity algorithm called α-partial isomorphic anonymity (α-PIA) to meet the privacy protection and data usage requirements in different scenarios of social networks. By capturing graph structure features at different levels to calculate the similarity between nodes, α-PIA can improve clustering quality. Extensive experiments are carried out based on two public datasets. Experimental results show that compared with similar schemes, α-PIA achieves better results in terms of information loss, average clustering coefficient and average shortest path length and better balances the privacy protection and practicality of graph data.