mlsanityData to 14 Oct 2026

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

arXiv 2306.02211 ↗First posted 3 Jun 2023cs.AIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 66 references that are in the map. First posted on arXiv 3 Jun 2023.

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5 of 66 references.

References · 5

  1. 2017A Survey of Indoor Localization Systems and Technologiescited by 78
  2. 2016CSI-based Fingerprinting for Indoor Localization: A Deep Learning Approachcited by 39
  3. 2019Effectiveness of Data Augmentation in Cellular-based Localization Using Deep Learningcited by 13
  4. 2023Privacy-preserving Pedestrian Tracking using Distributed 3D LiDARscited by 3
  5. 2013Communicating Is Crowdsourcing: Wi-Fi Indoor Localization with CSI-based Speed Estimationcited by 1

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