Lightweight Privacy-Preserving Scheme in WiFi Fingerprint-Based Indoor Localization

WiFi fingerprint-based localization, a common indoor localization technique, plays an important role in Internet of Things, supporting a number of mobile applications, e.g., navigation indoor, tracking, and so on. However, WiFi fingerprint-based localization suffers from the privacy issue, disclosing users’ location privacy and the data privacy of localization server (LS). Existing works cannot completely protect LS’s data privacy and moreover incur larger amount of computation and communication overhead. In this paper, we propose a LightWeight Privacy-Preserving scheme ( \(\mathrm {LWP}^{2}\) ) which protects both location privacy and data privacy with lower cost. The main idea of \(\mathrm {LWP}^{2}\) is to first formalize the privacy-preserving localization problem as minimizing the least-squared-error for an over-determined linear formulation and then design a lightweight solution in ciphertext space using the special structure of the over-determined linear formulation. Extensive experiments have validated the privacy preservation and the efficiency improvement of \(\mathrm {LWP}^{2}\) .