Privacy-Preserving Database Assisted Spectrum Access: A Socially-Aware Distributed Learning Approach

In this paper, we study a privacy-preserving spectrum sharing system to protect secondary users´ location privacy while enhancing spectrum access. The location privacy of secondary users can be compromised by an external adversary via the received signal strength (RSS)-based localization technique. To mitigate such privacy threat, we employ a random power perturbation approach that allows each secondary user to judiciously obfuscate the RSS captured by the adversary. While it can protect users´ location privacy, the power perturbation approach would inevitably degrade the system performance and bring challenges to the design of the spectrum allocation algorithm. In this work, we adopt a socially-aware database assisted spectrum access system and cast the spectrum allocation under users´ power perturbation as a stochastic channel selection game played among the users. To tackle the challenge brought by the privacy protection, we develop a two time-scale distributed learning algorithm, which is shown to converge almost surely to a socially-aware ε-Nash equilibrium. The numerical results show that the higher the privacy protection level is, the more significant the degradation of the network throughput would be.