Protecting Sensitive Label in Social Network
Privacy is one of the major concerns when publishing or sharing social network data. Privacy models are developed similar to k-anonymity to prevent node reidentification, but an attacker may still be able to infer one's private information if a group of nodes largely share the same sensitive labels (i.e., attributes). The novel anonymization methodology describes by adding noise nodes into the original graph with the consideration of introducing the least distortion to graph properties. The authors proposed an algorithm namely K-degree-L-diversity model for preserving social network data.