Privacy Preserving Algorithm for Spectrum Sensing in Cognitive Vehicle Networks

Abstract

The scarcity of spectrum resources fails to meet the increasing throughput demands of vehicular networks. There is an urgent need to maximize the utilization of spectrum bands in mobile networks. To ascertain the availability of spectrum bands, users should engage in wireless channel sensing and collaboration. However, spectrum sensing data always involves users' privacy, such as their location. This paper first introduces sensing trajectory inference attack in cognitive vehicular networks and then proposes a data confusion-based privacy-preserving algorithm and a cryptonym array-based privacy-preserving aggregation scheme for spectrum sensing in cognitive vehicular networks. Unlike existing methods, the proposed schemes transmit confused data during the aggregation process. This deliberate obfuscation makes it almost impossible to infer users' location from the transmitted data. The analysis demonstrates the resilience of the proposed schemes against sensing trajectory inference attack.