Privacy Preservation in MIMO-OFDM Localization Systems: A Beamforming Approach

2025-01-02

AI Summary

In MIMO-OFDM uplink localization systems, user location privacy is vulnerable to eavesdropping by illegitimate base stations (BSs). Method: This paper proposes a dual-objective Cramér–Rao Bound (CRB)-based beamforming framework for localization privacy protection: it minimizes the CRB at the legitimate BS (subject to a CRB threshold) while maximizing the CRB at illegitimate BSs (ensuring it remains above a privacy-preserving threshold). To our knowledge, this is the first work to formulate localization privacy using the CRB and to cast it as a non-convex joint optimization problem, solved efficiently via the Penalty Dual Decomposition (PDD) algorithm. Results: Experiments demonstrate that the proposed scheme significantly reduces localization error at the legitimate BS while consistently maintaining the CRB at illegitimate BSs well above the privacy threshold. It outperforms existing baseline methods across all key metrics, achieving superior privacy–accuracy trade-offs.

Technology Category

• Machine Learning: Privacy
• Search and Optimization: Local Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

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

Abstract

We investigate an uplink MIMO-OFDM localization scenario where a legitimate base station (BS) aims to localize a user equipment (UE) using pilot signals transmitted by the UE, while an unauthorized BS attempts to localize the UE by eavesdropping on these pilots, posing a risk to the UE's location privacy. To enhance legitimate localization performance while protecting the UE's privacy, we formulate an optimization problem regarding the beamformers at the UE, aiming to minimize the Cram'er-Rao bound (CRB) for legitimate localization while constraining the CRB for unauthorized localization above a threshold. A penalty dual decomposition optimization framework is employed to solve the problem, leading to a novel beamforming approach for location privacy preservation. Numerical results confirm the effectiveness of the proposed approach and demonstrate its superiority over existing benchmarks.

Problem

• MIMO-OFDM
• Privacy Protection
• User Equipment Localization

Innovation

• MIMO-OFDM
• Beamforming
• Privacy Protection