ConvXformer: Differentially Private Hybrid ConvNeXt-Transformer for Inertial Navigation
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866914107745107968 |
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| author | Tariq, Omer Bilal, Muhammad Hassan, Muneeb Ul Han, Dongsoo Crowcroft, Jon |
| author_facet | Tariq, Omer Bilal, Muhammad Hassan, Muneeb Ul Han, Dongsoo Crowcroft, Jon |
| contents | 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 $(ε,δ)$-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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19352 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ConvXformer: Differentially Private Hybrid ConvNeXt-Transformer for Inertial Navigation Tariq, Omer Bilal, Muhammad Hassan, Muneeb Ul Han, Dongsoo Crowcroft, Jon Machine Learning Cryptography and Security Robotics 68T07, 68T05, 68P27, 62M10 I.2.6; I.5.1; I.2.9; K.4.1; K.6.5; C.3; G.3 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 $(ε,δ)$-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. |
| title | ConvXformer: Differentially Private Hybrid ConvNeXt-Transformer for Inertial Navigation |
| topic | Machine Learning Cryptography and Security Robotics 68T07, 68T05, 68P27, 62M10 I.2.6; I.5.1; I.2.9; K.4.1; K.6.5; C.3; G.3 |
| url | https://arxiv.org/abs/2510.19352 |