Physical layer location privacy issue in wireless small cell networks

Abstract

High data rates are essential for next-generation wireless networks to support a growing number of computing devices and networking services. Small cell base station (SCBS) (e.g., picocells, microcells, femtocells) technology is a cost-effective solution to address this issue. However, one challenging issue with the increasingly dense network is the need for a distributed and scalable access point association protocol. In addition, the reduced cell size makes it easy for an adversary to map out the geographical locations of the mobile users, and hence breaching their location privacy. To address these issues, we establish a game-theoretic framework to develop a privacy-preserving stable matching algorithm that captures the large scale and heterogeneity nature of 5G networks. We show that without the privacy-preserving mechanism, an attacker can infer the location of the users by observing wireless connections and the knowledge of physical-layer system parameters. The protocol presented in this work provides a decentralized differentially private association algorithm which guarantees privacy to a large number of users in the network. We evaluate our algorithm using case studies, and demonstrate the tradeoff between privacy and system-wide performance for different privacy requirements and a varying number of mobile users in the network. Our simulation results corroborate the result that the total number of mobile users should be lower than the overall network capacity to achieve desirable levels of privacy and QoS.