Lightweight Privacy-Preserving Scheme in Wi-Fi Fingerprint-Based Indoor Localization

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