Protecting Sensitive Labels in Social Network Data Anonymization

Protecting Sensitive Labels in Social Network Data Anonymization ABSTRACT: Privacy is one of the major concerns when publishing or sharing social network data for social science research and business analysis. Recently, researchers have developed privacy models similar to k-anonymity to prevent node reidentification through structure information. However, even when these privacy models are enforced, an […]


m-Privacy for Collaborative Data Publishing

m-Privacy for Collaborative Data Publishing ABSTRACT: In this paper, we consider the collaborative data publishing problem for anonymizing horizontally partitioned data at multiple data providers. We consider a new type of “insider attack” by colluding data providers who may use their own data records (a subset of the overall data) to infer the data records […]