Mobility data anonymization by obfuscating the cellular network topology graph

Studies of cellular-network data, comprising analyses of user movements across the network, are becoming increasingly popular with the rise of new services based on user behavior and geographic location. Such data might contain, for example, timestamped lists of IDs of cells a user was registered to. If coupled with the cell locations, confidential to the operator, this would enable reconstruction of user trajectories across the regions covered. It is vital to preserve privacy of this data for all parts involved (users, operators) while enabling open sharing of the data to foster research and development of new findings, services and technologies based on mining of this data. Thus, we pose the problem of obfuscating entire cellular-network topologies while retaining some of the analytical value of the user data. To this end, we adopt multiple obfuscation methods based on different topology-graph distortions, and analyze their performance using simulated scenarios.