Paper 2022/564

FAPRIL: Towards Faster Privacy-Preserving Fingerprint-Based Localization

Christopher van der Beets, Raine Nieminen, and Thomas Schneider

Abstract

Fingerprinting is a commonly used technique to provide accurate localization for indoor areas, where global navigation satellite systems, such as GPS and Galileo, cannot function or are not precise enough. Although fingerprint-based indoor localization has gained wide popularity, existing solutions that preserve privacy either rely on non-colluding servers or have high communication which hinder deployment. In this work we present FAPRIL, a privacy-preserving indoor localization scheme, which takes advantage of the latest secure two-party computation protocol improvements. We can split our scheme into two parts: an input independent setup phase and an online phase. We concentrate on optimizing the online phase for mobile clients who run on a mobile data plan and observe that recurring operands allow to optimize the total communication overhead even further. Our observation can be generalized, e.g., to improve multiplication of Arithmetic secret shared matrices. We implement FAPRIL on mobile devices and our benchmarks over a simulated LTE network show that the online phase of a private localization takes under 0.15 seconds with less than 0.20 megabytes of communication even for large buildings. The setup phase, which can be pre-computed, depends heavily on the setting but stays in the range 0.28 - 4.14 seconds and 0.69 - 16.00 megabytes per localization query. The round complexity of FAPRIL is constant for both phases.