Privacy-Preserving Machine Learning in IoT: A Study of Data Obfuscation Methods

Yonan Yonan, Mohammad O. Abdullah, Felix Nilsson, Mahdi Fazeli, Ahmad Patooghy, Slawomir Nowaczyk

2025

Abstract

In today’s interconnected digital world, ensuring data privacy is critical, particularly for neural networks operating remotely in the age of the Internet of Things (IoT). This paper tackles the challenge of data privacy preservation in IoT environments by investigating Utility-Preserving Data Transformation (UPDT) methods, which aim to transform data in ways that reduce or eliminate sensitive information while retaining its utility for analytical tasks. UPDT methods aim to balance privacy preservation and utility in data analytics. This study examines the strengths and limitations of these methods, focusing on ObfNet, a neural network-based obfuscation algorithm, as a representative case study to contextualize our analysis. By analyzing ObfNet, we highlight its vulnerabilities and based on these findings we introduce LightNet and DenseNet as novel neural networks to identify ObfNet’s limitations, particularly for larger and more complex data. We uncover challenges such as information leakage and explore the implications for maintaining privacy during remote neural network inference. Our findings underscore the challenges and possibilities to preserve privacy during remote neural network inference for UPDT algorithms, especially in resource-limited edge devices.

Paper Citation

in Harvard Style

Yonan Y., Abdullah M., Nilsson F., Fazeli M., Patooghy A. and Nowaczyk S. (2025). Privacy-Preserving Machine Learning in IoT: A Study of Data Obfuscation Methods. In Proceedings of the 22nd International Conference on Security and Cryptography - Volume 1: SECRYPT; ISBN 978-989-758-760-3, SciTePress, pages 347-354. DOI: 10.5220/0013458900003979

in Bibtex Style

@conference{secrypt25,
author={Yonan Yonan and Mohammad Abdullah and Felix Nilsson and Mahdi Fazeli and Ahmad Patooghy and Slawomir Nowaczyk},
title={Privacy-Preserving Machine Learning in IoT: A Study of Data Obfuscation Methods},
booktitle={Proceedings of the 22nd International Conference on Security and Cryptography - Volume 1: SECRYPT},
year={2025},
pages={347-354},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013458900003979},
isbn={978-989-758-760-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Security and Cryptography - Volume 1: SECRYPT
TI - Privacy-Preserving Machine Learning in IoT: A Study of Data Obfuscation Methods
SN - 978-989-758-760-3
AU - Yonan Y.
AU - Abdullah M.
AU - Nilsson F.
AU - Fazeli M.
AU - Patooghy A.
AU - Nowaczyk S.
PY - 2025
SP - 347
EP - 354
DO - 10.5220/0013458900003979
PB - SciTePress