Privacy-preserving WiFi Fingerprint Localization Based on Spatial Linear Correlation

Abstract

With the widespread deployment of IoT (Internet of Things) devices, WiFi fingerprint-based localization is becoming one of the most promising techniques for indoor localization. A client is able to obtain its location by providing its measured fingerprint (vector of WiFi signal strengths) to the service provider who maps the fingerprint against the database and returns the result back to the client. However, traditional applications of WiFi fingerprint-based localization may disclose the client’s location privacy and often incur high consumption of communication and computing resources. In this paper, we focus on implementing a privacy-preserving framework with high efficiency and accuracy for WiFi fingerprint-based localization. Firstly, to reduce computational overhead at the server side, we introduce a clustering algorithm called k-means++ in offline phase. Besides, we explore the correlation of the fingerprint and propose a Pearson correlation based distance computation method, which achieves better accuracy than traditional Euclidean distance. Finally, we secure the overall computation by adapting a series of secure multi-party computing primitives. Theoretical analysis is carried out to prove the security of our scheme. Experiments on real-world datasets indicate that our scheme achieves better practicality and efficiency compared with existing methods. Compared to existing work PriWFL and PPWFL, our scheme reduces the average distance error by approximately 4.5% and 2.9% under a query time of less than 0.2s.