A privacy-preserving device tracking system using a low-power wide-area network

Tomer Ashur, Jeroen Delvaux, Sanghan Lee, Pieter Maene, Eduard Marin, Svetla Nikova, Oscar Reparaz, Vladimir Rožić, Dave Singelée, Bohan Yang, Bart Preneel

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)


This paper presents the design and implementation of a low-power privacy-preserving device tracking system based on Internet of Things (IOT) technology. The system consists of low-power nodes and a set of dedicated beacons. Each tracking node broadcasts pseudonyms and encrypted versions of observed beacon identifiers over a Low-Power Wide-Area Network (LPWAN). Unlike most commercial systems, our solution ensures that the device owners are the only ones who can locate their devices. We present a detailed design and validate the result with a prototype implementation that considers power and energy consumption as well as side-channel attacks. Our implementation uses Physically Unclonable Function (PUF) technology for secure key-storage in an innovative way. We build and evaluate a complete demonstrator with off-the-shelf IoT nodes, Bluetooth Low Energy (BLE) beacons, and LoRa long distance communication (LPWAN). We validate the setup for a bicycle tracking application and also estimate the requirements for a low-cost ASIC node.
Original languageEnglish
Title of host publicationCryptology and Network Security - 16th International Conference, CANS 2017, Revised Selected Papers
EditorsSrdjan Capkun, Sherman S.M. Chow
Place of PublicationCham
Number of pages23
ISBN (Electronic)978-3-030-02641-7
ISBN (Print)978-3-030-02640-0
Publication statusPublished - 1 Jan 2018
Externally publishedYes
EventCANS 2017 - Hong Kong, China
Duration: 30 Nov 20172 Dec 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11261 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceCANS 2017
CityHong Kong


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