Bilateral Privacy-Preserving Utility Maximization Protocol in Database-Driven Cognitive Radio Networks

Abstract

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 PUs and SUs. The main challenge lies in how to coordinate PUs and SUs to maximize their utilities provided that their location privacy is protected. We first introduce a quantitative method to calculate both PUs’ and SUs’ location privacy, and then design a novel privacy preserving Utility Maximization protocol (UMax). UMax allows for both PUs and SUs to adjust their privacy preserving levels and optimize transmit power iteratively to achieve the maximum utilities. Through extensive evaluations, we demonstrate that our proposed protocol can efficiently increase the utilities of both PUs and SUs while preserving their location privacy.