Physical Layer Location Privacy in SIMO Communication Using Fake Paths Injection

2024-02-02

arXiv.org

AI Summary

This work addresses the physical-layer location privacy threat in single-input multiple-output (SIMO) systems, where an eavesdropper (Eve) estimates the transmitter’s physical location via multipath signal analysis. To counter this, we propose a physical-layer privacy-preserving mechanism based on spoofed-path injection: the transmitter actively injects controllable, artificial multipath components. Leveraging a uniform linear antenna array and direction-of-arrival (DoA) estimation, we model the fundamental localization accuracy limit using the Cramér–Rao bound (CRB) and introduce—novelty—the CRB eigenvalue ratio as an analytically tractable privacy metric. Theoretically, we prove that privacy margin scales inversely with the square of the angular separation between true and spoofed paths, and rigorously characterize the privacy boundary via spectral properties of generalized Vandermonde matrices. Simulations confirm minimal impact on the legitimate receiver’s (Bob’s) bit error rate, while Eve’s localization error increases significantly; empirical results closely match CRB predictions.

Technology Category

• Machine Learning: Privacy
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Localization, Mapping, and Navigation

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

Fake path injection is an emerging paradigm for inducing privacy over wireless networks. In this paper, fake paths are injected by the transmitters into a single-input multiple-output (SIMO) communication channel to obscure their physical location from an eavesdropper. The case where the receiver (Bob) and the eavesdropper (Eve) use a linear uniform array to locate the transmitter's (Alice) position is considered. A novel statistical privacy metric is defined as the ratio between the smallest (resp. largest) eigenvalues of Eve's (resp. Bob's) Cram'er-Rao lower bound (CRB) on the SIMO channel parameters to assess the privacy enhancements. Leveraging the spectral properties of generalized Vandermonde matrices, bounds on the privacy margin of the proposed scheme are derived. Specifically, it is shown that the privacy margin increases quadratically in the inverse of the angular separation between the true and the fake paths under Eve's perspective. Numerical simulations validate the theoretical findings on CRBs and showcase the approach's benefit in terms of bit error rates achievable by Bob and Eve.

Problem

• Enhancing location privacy in SIMO
• Using fake path injection
• Novel statistical privacy metric

Innovation

• Fake path injection for privacy
• Statistical privacy metric using CRB
• SIMO channel with Vandermonde matrices