K-anonymity in indoor spaces through hierarchical graphs

Due to complex structure of indoor space, the demand on LBS (Location Based Services) in indoor space has been increasing as well as outdoor. Although LBS give convenience for users, they still have problems of exposing personal location and privacy. In order to protect privacy, many researches have been done, among which location K-anonymity is a method by cloaking locations through ASR (Anonymizing Spatial Region) involving K-1 other users. However there is a limitation of this method to apply in indoor space that it assumes Euclidean Space and indoor space is characterized as non-Euclidean space in most cases unlike outdoor space. In this paper, we propose a new approach to location K-anonymity in indoor space. Our approach is based on the hierarchical structure of indoor space. First, we propose several algorithms to construct hierarchical structures for a given indoor space. Second, we introduce ASR generation algorithms to ensure the location K-anonymity with hierarchical structures. We analyze our methods through experimental analysis.