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.

本篇论文介绍了一种名为PassiFi的新型室内定位系统，采用了无源Wi-Fi到达时间差（TDoA）方法来确保用户隐私和数据完整性，同时利用深度神经网络来学习TDoA和位置之间的复杂关系，评估结果表明PassiFi在保护隐私的同时，精度达到了与最先进的主动测量系统相当的亚米级别，超过了传统的多边定位系统128%。

隐私保护设计: 基于Wi-Fi被动TDOA的室内定位系统