On Preserving Private Geosocial Networks against Practical Attacks

GeoSocial Networks (GSNs) are becoming increasingly popular due to its power in providing high-performance and flexible service capabilities. More and more internet users have accepted this innovative service model. However, although GSNs have great business value for data analysis by integrated with location information, it may seriously compromise users' privacy in publishing the GSN data. In this paper, we study the identity disclosure problem in publishing GSN data. We first discuss the attack problem by considering location-based properties as background knowledge, and then formalize an attack model, named (k, m)-anonymity. Then we propose a complete solution to achieve (k, m)-anonymization to prevent the released data from the attack. We also take data utility into consideration by defining specific information loss metrics. The performances of the methods have been validated by the real-world data.