When the Hammer Meets the Nail: Multi-Server PIR for Database-Driven CRN with Location Privacy Assurance
This addresses location privacy for users in cognitive radio networks, offering a significant improvement over prior methods by leveraging existing system architecture without extra costs.
The paper tackles the problem of achieving location privacy for secondary users in database-driven cognitive radio networks, demonstrating that multi-server private information retrieval can provide information-theoretic privacy with end-to-end delays under a second, outperforming existing single-server methods by a magnitude of times.
We show that it is possible to achieve information theoretic location privacy for secondary users (SUs) in database-driven cognitive radio networks (CRNs) with an end-to-end delay less than a second, which is significantly better than that of the existing alternatives offering only a computational privacy. This is achieved based on a keen observation that, by the requirement of Federal Communications Commission (FCC), all certified spectrum databases synchronize their records. Hence, the same copy of spectrum database is available through multiple (distinct) providers. We harness the synergy between multi-server private information retrieval (PIR) and database- driven CRN architecture to offer an optimal level of privacy with high efficiency by exploiting this observation. We demonstrated, analytically and experimentally with deployments on actual cloud systems that, our adaptations of multi-server PIR outperform that of the (currently) fastest single-server PIR by a magnitude of times with information theoretic security, collusion resiliency, and fault-tolerance features. Our analysis indicates that multi-server PIR is an ideal cryptographic tool to provide location privacy in database-driven CRNs, in which the requirement of replicated databases is a natural part of the system architecture, and therefore SUs can enjoy all advantages of multi-server PIR without any additional architectural and deployment costs.