Optimal strategies for defending location inference attack in database-driven CRNs

Database-driven Cognitive Radio Network (CRN) has been proposed to replace the requirement of spectrum sensing of terminal devices so that the operation of users is simplified. However, location privacy issues introduce a big challenge for securing database-driven CRN due to spectrum availability information. The existing works consider either PU or SU's location privacy while not the both. In this study, we identify a unified attack framework in which a curious user could infer a target's location based on the spectrum availability/utilization information. Further, we propose a location privacy protection mechanism, which allows both SU and PU to protect their location privacy by adopting a series of countermeasures. The location privacy and spectrum utility are the trade-off. In the countermeasures of location privacy preserving spectrum query process, both SU and database aim to maximize the location privacy with constraints of spectrum utility. Thus, they can obtain higher location privacy level with sacrifice of spectrum utility as long as the spectrum utility meets the requirements. We evaluate the unified attack and defence approaches based on simulation and demonstrate the effectiveness of the proposed location privacy preserving approaches.