Differentially Private Feature Release for Wireless Sensing: Adaptive Privacy Budget Allocation on CSI Spectrograms

• URL: http://arxiv.org/abs/2512.20323v3
• Date: Mon, 29 Dec 2025 12:08:34 GMT
• Title: Differentially Private Feature Release for Wireless Sensing: Adaptive Privacy Budget Allocation on CSI Spectrograms
• Authors: Ipek Sena Yilmaz, Onur G. Tuncer, Zeynep E. Aksoy, Zeynep Yağmur Baydemir,
• Abstract summary: We study differentially private (DP) feature release for wireless sensing.<n>We propose an adaptive privacy budget allocation mechanism tailored to the highly non-uniform structure of CSI time-frequency representations.<n>Our method yields higher accuracy and lower error while substantially reducing empirical leakage in identity and membership inference attacks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Wi-Fi/RF-based human sensing has achieved remarkable progress with deep learning, yet practical deployments increasingly require feature sharing for cloud analytics, collaborative training, or benchmark evaluation. Releasing intermediate representations such as CSI spectrograms can inadvertently expose sensitive information, including user identity, location, and membership, motivating formal privacy guarantees. In this paper, we study differentially private (DP) feature release for wireless sensing and propose an adaptive privacy budget allocation mechanism tailored to the highly non-uniform structure of CSI time-frequency representations. Our pipeline converts CSI to bounded spectrogram features, applies sensitivity control via clipping, estimates task-relevant importance over the time-frequency plane, and allocates a global privacy budget across spectrogram blocks before injecting calibrated Gaussian noise. Experiments on multi-user activity sensing (WiMANS), multi-person 3D pose estimation (Person-in-WiFi 3D), and respiration monitoring (Resp-CSI) show that adaptive allocation consistently improves the privacy-utility frontier over uniform perturbation under the same privacy budget. Our method yields higher accuracy and lower error while substantially reducing empirical leakage in identity and membership inference attacks.

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