Privacy Preservation for Machine Learning in IIoT Data via Manifold Learning and Elementary Row Operations

E. Fatih Yetkin, Tuğçe Ballı

2025

Abstract

Modern large-scale production sites are highly data-driven and need large computational power due to the amount of the data collected. Hence, relying only on in-house computing systems for computational workflows is not always feasible. Instead, cloud environments are often preferred due to their ability to provide scalable and on-demand access to extensive computational resources. While cloud-based workflows offer numerous advantages, concerns regarding data privacy remain a significant obstacle to their widespread adoption, particularly in scenarios involving sensitive data and operations. This study aims to develop a computationally efficient privacy protection (PP) approach based on manifold learning and the elementary row operations inspired from the lower-upper (LU) decomposition. This approach seeks to enhance the security of data collected from industrial environments, along with the associated machine learning models, thereby protecting sensitive information against potential threats posed by both external and internal adversaries within the collaborative computing environment.

Paper Citation

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP
TI - Privacy Preservation for Machine Learning in IIoT Data via Manifold Learning and Elementary Row Operations
SN - 978-989-758-735-1
AU - Yetkin E.
AU - Ballı T.
PY - 2025
SP - 607
EP - 614
DO - 10.5220/0013275000003899
PB - SciTePress

in Harvard Style

Yetkin E. and Ballı T. (2025). Privacy Preservation for Machine Learning in IIoT Data via Manifold Learning and Elementary Row Operations. In Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP; ISBN 978-989-758-735-1, SciTePress, pages 607-614. DOI: 10.5220/0013275000003899

in Bibtex Style

@conference{icissp25,
author={E. Yetkin and Tuğçe Ballı},
title={Privacy Preservation for Machine Learning in IIoT Data via Manifold Learning and Elementary Row Operations},
booktitle={Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP},
year={2025},
pages={607-614},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013275000003899},
isbn={978-989-758-735-1},
}