Towards Privacy-Preserving Aggregation for Collaborative Spectrum Sensing

Abstract

Collaborative spectrum sensing has become increasingly popular in cognitive radio networks to enable unlicensed secondary users to coexist with the licensed primary users and share spectrum without interference. Despite its promise in performance enhancement, collaborative sensing is still facing a lot of security challenges. The problem of revealing secondary users’ location information through sensing reports has been reported recently. Unlike any existing work, in this paper we not only address the location privacy issue in the collaborative sensing to be against semi-honest adversaries, but also take malicious adversaries into consideration. We propose efficient schemes to protect secondary users’ reports from being revealed in the aggregation process at the fusion center. We rigorously prove that our privacy-preserving collaborative sensing schemes are secure against attacks from both the fusion center and secondary users. We also evaluate our schemes extensively and verify its efficiency and feasibility.