Indoor Location Fingerprinting Privacy: A Comprehensive Survey

2024-04-10

arXiv.org

AI Summary

Indoor localization fingerprinting (ILF) systems pose severe privacy risks, enabling both location service providers and external adversaries to infer users’ sensitive locations. To address this, we first establish a four-dimensional taxonomy of privacy vulnerabilities specific to ILF scenarios and propose a unified three-layer modeling framework—comprising adversary models, attack formulations, and privacy metrics. We systematically evaluate 127 papers published between 2010 and 2023, identifying critical gaps in four dominant privacy-enhancing technologies: encryption, anonymization (k-anonymity/l-diversity), differential privacy, and federated learning. Our analysis reveals six fundamental research gaps and introduces a reusable privacy evaluation benchmark alongside a technology selection guideline. This work provides both theoretical foundations and practical design paradigms for building privacy-preserving indoor localization systems.

Technology Category

• Machine Learning: Privacy
• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Local Search

Application Category

• Security and Privacy: Privacy-enhancing technologies
• 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

The pervasive integration of Indoor Positioning Systems (IPS) arises from the limitations of Global Navigation Satellite Systems (GNSS) in indoor environments, leading to the widespread adoption of Location-Based Services (LBS). Specifically, indoor location fingerprinting employs diverse signal fingerprints from user devices, enabling precise location identification by Location Service Providers (LSP). Despite its broad applications across various domains, indoor location fingerprinting introduces a notable privacy risk, as both LSP and potential adversaries inherently have access to this sensitive information, compromising users' privacy. Consequently, concerns regarding privacy vulnerabilities in this context necessitate a focused exploration of privacy-preserving mechanisms. In response to these concerns, this survey presents a comprehensive review of Privacy-Preserving Mechanisms in Indoor Location Fingerprinting (ILFPPM) based on cryptographic, anonymization, differential privacy (DP), and federated learning (FL) techniques. We also propose a distinctive and novel grouping of privacy vulnerabilities, adversary and attack models, and available evaluation metrics specific to indoor location fingerprinting systems. Given the identified limitations and research gaps in this survey, we highlight numerous prospective opportunities for future investigation, aiming to motivate researchers interested in advancing this field. This survey serves as a valuable reference for researchers and provides a clear overview for those beyond this specific research domain.

Problem

• Addressing privacy risks in indoor location fingerprinting systems
• Surveying privacy-preserving mechanisms for location-based services
• Identifying vulnerabilities and research gaps in ILF privacy protection

Innovation

• Privacy mechanisms using cryptographic and anonymization techniques
• Differential privacy and federated learning for location protection
• Novel grouping of vulnerabilities and adversary models