A Privacy-Preserving Localization Scheme with Node Selection in Mobile Networks

2026-01-07

arXiv.org

AI Summary

This work addresses the vulnerability of location privacy in mobile networks, where positioning processes often expose the locations of both target nodes and anchor nodes. Existing privacy-preserving approaches typically compromise localization accuracy or incur excessive communication overhead. To overcome these limitations, this paper proposes PPLZN, a novel scheme that integrates secure multi-party computation techniques with a privacy-aware node selection mechanism tailored for crowdsourced localization. PPLZN simultaneously protects the location privacy of both parties while significantly enhancing localization accuracy and reducing communication and computational costs. Extensive simulations demonstrate that PPLZN outperforms state-of-the-art methods in both positioning precision and communication efficiency, making it well-suited for large-scale mobile networks with stringent resource constraints.

Technology Category

• Machine Learning: Privacy
• Search and Optimization: Local Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

Abstract

Localization in mobile networks has been widely applied in many scenarios. However, an entity responsible for location estimation exposes both the target and anchors to potential location leakage at any time, creating serious security risks. Although existing studies have proposed privacy-preserving localization algorithms, they still face challenges of insufficient positioning accuracy and excessive communication overhead. In this article, we propose a privacy-preserving localization scheme, named PPLZN. PPLZN protects protects the location privacy of both the target and anchor nodes in crowdsourced localization. Simulation results validate the effectiveness of PPLZN. Evidently, it can achieve accurate position estimation without location leakage and outperform state-of-the-art approaches in both positioning accuracy and communication overhead. In addition, PPLZN significantly reduces computational and communication overhead in large-scale deployments, making it well-fitted for practical privacy-preserving localization in resource-constrained networks.

Problem

• privacy-preserving localization
• location privacy
• mobile networks
• positioning accuracy
• communication overhead

Innovation

• privacy-preserving localization
• node selection
• location privacy
• communication overhead
• crowdsourced localization