Maximizing Privacy and Security of Collaborative Indoor Positioning using Zero-Knowledge Proofs
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Date
2023-04-26
Authors
Casanova-Marqués, Raúl
Torres-Sospedra, Joaquín
Hajný, Jan
Gould, Michael
0000-0001-6653-867XAdvisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Altmetrics
Abstract
The increasing popularity of wearable-based Collaborative Indoor Positioning Systems (CIPSs) has led to the development of new methods for improving positioning accuracy. However, these systems often rely on protocols, such as iBeacon, that lack sufficient privacy protection. In addition, they depend on centralized entities for the authentication and verification processes. To address the limitations of existing protocols, this paper presents a groundbreaking contribution to the field of wearable-based CIPSs. We propose a decentralized Attribute-based Authentication (ABA) protocol that offers superior levels of privacy protection, untraceability, and unlinkability of user actions. Unlike existing protocols that rely on centralized entities, our approach leverages decentralized mechanisms for authentication and verification, ensuring the privacy of user location data exchange. Through extensive experimentation across multiple platforms, our results demonstrate the practicality and feasibility of the proposed protocol for real-world deployment. Overall, this work opens up new avenues for secure and privacy-preserving wearable-based CIPSs, with potential implications for the rapidly growing field of Internet of Things (IoT) applications.
Description
Citation
Internet of Things. 2023, vol. 22, issue neuvedeno, p. 1-18.
https://www.sciencedirect.com/science/article/pii/S2542660523001245
https://www.sciencedirect.com/science/article/pii/S2542660523001245
Document type
Peer-reviewed
Document version
Published version
Date of access to the full text
Language of document
en