In this paper, we propose new location privacy preserving schemes for database-driven cognitive radio networks that protect secondary users’ (SUs) location privacy while allowing them to learn spectrum availability in their vicinity. Our schemes harness probabilistic set membership data structures to exploit the structured nature of spectrum databases (DBs) and SUs’ queries. This enables us to create a compact representation of DB that could be queried by SUs without having to share their location with DB, thus guaranteeing their location privacy. Our proposed schemes offer different cost-performance characteristics. Our first scheme relies on a simple yet powerful two-party protocol that achieves unconditional security with a plausible communication overhead by making DB send a compacted version of its content to SU which needs only to query this data structure to learn spectrum availability. Our second scheme achieves significantly lower communication and computation overhead for SUs, but requires an additional architectural entity which receives the compacted version of the database and fetches the spectrum availability information in lieu of SUs to alleviate the overhead on the latter. We show that our schemes are secure, and also demonstrate that they offer significant advantages over existing alternatives for various performance and or security metrics.
Database-driven cognitive radio has been well recognized as an efficient way to reduce interference between Primary Users (PUs) and Secondary Users (SUs). In database-driven cognitive radio, PUs and SUs must provide their locations to enable dynamic channel allocation, which raises location privacy breach concern. Previous studies only focus on unilateral privacy preservation, i.e., Only PUs' or SUs' privacy is preserved. In this paper, we propose to protect bilateral location privacy of a PU and an SU. The main challenge lies in how to coordinate the PU and SU to maximize their utility provided that their location privacy is protected. We first introduce a quantitative method to calculate both PU's and SU's location privacy, and then design a novel privacy preserving Utility Maximization protocol (UMax). UMax allows for both PU and SU to adjust their privacy preserving levels and optimize transmit power iteratively to achieve the maximum utility. Through extensive evaluations, we demonstrate that our proposed mechanism can efficiently increase the utility of both PU and SU while preserving their location privacy. The growing popularity of location-based systems, allowing unknown untrusted servers to easily collect huge amounts of information regarding users' location, has recently started raising serious privacy concerns. In this paper we introduce geoind, a formal notion of privacy for location-based systems that protects the user's exact location, while allowing approximate information -- typically needed to obtain a certain desired service -- to be released. This privacy definition formalizes the intuitive notion of protecting the user's location within a radius @math with a level of privacy that depends on r, and corresponds to a generalized version of the well-known concept of differential privacy. Furthermore, we present a mechanism for achieving geoind by adding controlled random noise to the user's location. We describe how to use our mechanism to enhance LBS applications with geo-indistinguishability guarantees without compromising the quality of the application results. Finally, we compare state-of-the-art mechanisms from the literature with ours. It turns out that, among all mechanisms independent of the prior, our mechanism offers the best privacy guarantees.