Privacy-Preserving by Design: Indoor Positioning System Using Wi-Fi Passive TDOA

TL;DR

PassiFi is a passive Wi-Fi TDoA indoor localization system that achieves high accuracy and privacy preservation by using deep learning and fingerprinting, outperforming traditional methods in real-world tests.

Contribution

This paper introduces PassiFi, a passive Wi-Fi TDoA system that balances privacy and accuracy using deep neural networks and fingerprinting techniques.

Findings

Surpasses traditional multilateration by 128% in accuracy

Achieves sub-meter localization accuracy

Effectively preserves user privacy during localization

Abstract

Indoor localization systems have become increasingly important in a wide range of applications, including industry, security, logistics, and emergency services. However, the growing demand for accurate localization has heightened concerns over privacy, as many localization systems rely on active signals that can be misused by an adversary to track users' movements or manipulate their measurements. This paper presents PassiFi, a novel passive Wi-Fi time-based indoor localization system that effectively balances accuracy and privacy. PassiFi uses a passive WiFi Time Difference of Arrival (TDoA) approach that ensures users' privacy and safeguards the integrity of their measurement data while still achieving high accuracy. The system adopts a fingerprinting approach to address multi-path and non-line-of-sight problems and utilizes deep neural networks to learn the complex relationship between TDoA and location. Evaluation in a real-world testbed demonstrates PassiFi's exceptional performance, surpassing traditional multilateration by 128%, achieving sub-meter accuracy on par with state-of-the-art active measurement systems, all while preserving privacy.