ConvXformer: Differentially Private Hybrid ConvNeXt-Transformer for Inertial Navigation

• URL: http://arxiv.org/abs/2510.19352v1
• Date: Wed, 22 Oct 2025 08:20:31 GMT
• Title: ConvXformer: Differentially Private Hybrid ConvNeXt-Transformer for Inertial Navigation
• Authors: Omer Tariq, Muhammad Bilal, Muneeb Ul Hassan, Dongsoo Han, Jon Crowcroft,
• Abstract summary: Deep learning-based inertial tracking systems remain vulnerable to privacy breaches.<n>We propose ConvXformer, a hybrid architecture that fuses ConvNeXt blocks with Transformer encoders in a hierarchical structure for robust inertial navigation.
• Score: 4.540223607965505
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data-driven inertial sequence learning has revolutionized navigation in GPS-denied environments, offering superior odometric resolution compared to traditional Bayesian methods. However, deep learning-based inertial tracking systems remain vulnerable to privacy breaches that can expose sensitive training data. \hl{Existing differential privacy solutions often compromise model performance by introducing excessive noise, particularly in high-frequency inertial measurements.} In this article, we propose ConvXformer, a hybrid architecture that fuses ConvNeXt blocks with Transformer encoders in a hierarchical structure for robust inertial navigation. We propose an efficient differential privacy mechanism incorporating adaptive gradient clipping and gradient-aligned noise injection (GANI) to protect sensitive information while ensuring model performance. Our framework leverages truncated singular value decomposition for gradient processing, enabling precise control over the privacy-utility trade-off. Comprehensive performance evaluations on benchmark datasets (OxIOD, RIDI, RoNIN) demonstrate that ConvXformer surpasses state-of-the-art methods, achieving more than 40% improvement in positioning accuracy while ensuring $(\epsilon,\delta)$-differential privacy guarantees. To validate real-world performance, we introduce the Mech-IO dataset, collected from the mechanical engineering building at KAIST, where intense magnetic fields from industrial equipment induce significant sensor perturbations. This demonstrated robustness under severe environmental distortions makes our framework well-suited for secure and intelligent navigation in cyber-physical systems.

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