摘要 : Nowadays, social networks provide users an interactive platform to create and share heterogeneouscontent for a lot of different purposes (eg, to comment events and facts, and express andshare personal opinions on specific topics),... 展开 Nowadays, social networks provide users an interactive platform to create and share heterogeneouscontent for a lot of different purposes (eg, to comment events and facts, and express andshare personal opinions on specific topics), allowing millions of individuals to create online profilesand share personal information with vast networks known and sometimes also unknownpeople. Knowledge about users, content, and relationships in a social network may be used foran adversary attack of some victims easily. Although a number of works have been done for dataprivacy preservation on relational data, they cannot be applied in social networks and in generalfor big data analytics. In this paper, we first propose a novel data model that integrates andcombines information on users belonging to 1 or more heterogeneous online social networks,together with the content that is generated, shared, and used within the related environments,using an hypergraph data structure; then we implemented the most diffused centrality measuresand also introduced a new centrality measure—based on the concept of “neighborhood”among users—that may be efficiently applied for a number of data privacy issues, such as lurkersand neighborhood attack prevention, especially in “interest-based” social networks. Someexperiments using the Yelp dataset are discussed. 收起