Practical Anonymization for Protecting Privacy in Combinatorial Maps

Combinatorial Map (CM) is becoming increasingly popular due to its power in modeling topological structures with subdivided objects, which is widely used in the fields of social network, computer vision, social media and so on. However, due to its specific structural properties, an unprotected release of a combinatorial map may cause the identity disclosure problem, which is a major privacy breach revealing the identification of entities with certain background knowledge known by an adversary. In this paper, we discuss the privacy preserving problem in publishing private combinatorial maps. We first formalize a specific anonymizing model to deal with dart-related attacks, and discuss an efficient metric to quantify information loss incurred in the perturbation. Then we propose an efficient method for the dart anonymization problem to prevent a CM from the attack. Our approaches are efficient and practical, and have been validated by extensive experiments on two sets of synthetic data.