Protecting Human Activity Signatures in Compressed IEEE 802.11 CSI Feedback

2025-12-20

AI Summary

IEEE 802.11 explicit CSI feedback transmits Givens rotation parameters and phase angles in plaintext, exposing sensitive information—including user activity, identity, and location. Method: We propose a standards-compatible differential privacy (DP) quantization mechanism: (i) the first ε-differentially private randomized quantizer tailored for Givens parameters; (ii) the first closed-form derivation of global sensitivity for right singular subspace angular representations, enabling tunable privacy calibration; all while preserving the standard 802.11 feedback structure. Contribution/Results: Our mechanism provides rigorous, mathematically guaranteed privacy under DP, with negligible impact on beamforming performance—simulations show less than 0.5 dB degradation in signal-to-interference-plus-noise ratio (SINR). It thus achieves a favorable trade-off between strong privacy protection and communication efficiency, without requiring modifications to existing PHY/MAC layer protocols.

Technology Category

• Machine Learning: Privacy
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Explicit channel state information (CSI) feedback in IEEE~802.11 conveys emph{transmit beamforming directions} by reporting quantized Givens rotation and phase angles that parametrize the right-singular subspace of the channel matrix. Because these angles encode fine-grained spatial signatures of the propagation environment, recent work have shown that plaintext CSI feedback can inadvertently reveal user activity, identity, and location to passive eavesdroppers. In this work, we introduce a standards-compatible emph{differentially private (DP) quantization mechanism} that replaces deterministic angular quantization with an $varepsilon$-DP stochastic quantizer applied directly to the Givens parameters of the transmit beamforming matrix. The mechanism preserves the 802.11 feedback structure, admits closed-form sensitivity bounds for the angular representation, and enables principled privacy calibration. Numerical simulations demonstrate strong privacy guarantees with minimal degradation in beamforming performance.

Problem

• Protects user activity from eavesdroppers via differential privacy
• Secures compressed CSI feedback in IEEE 802.11 standards
• Prevents leakage of location and identity in wireless channels

Innovation

• Differentially private stochastic quantizer for Givens angles
• Preserves 802.11 feedback structure and sensitivity bounds
• Minimizes beamforming performance loss with privacy guarantees