Addressing Privacy and Security Challenges at the Industry 5.0 Human-Intensive and Highly Automated Factory Floor

With Industry 5.0, prioritizing worker well-being, the integration of human-related data into production processes poses challenges related to data privacy and compliance with regulations such as GDPR. This paper presents a comprehensive approach to anonymizing sensitive data collected from wearable devices. We propose a methodology that combines real-time processing and complex anonymization algorithms, ensuring end-to-end protection. We employ techniques such as normalization, hashing, and feature dropping. Our findings contribute to advancing privacy-preserving techniques in Industry 5.0 and underscore the importance of safeguarding user privacy in the era of advanced manufacturing.

Authors

Institutions

Publication Details

Published
2024-11-03
DOI
https://doi.org/10.1109/iecon55916.2024.10905116
Citations
2
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
0.70

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Addressing Privacy and Security Challenges at the Industry 5.0 Human-Intensive and Highly Automated Factory Floor

Valeriy Vyatkin, Udayanto Dwi Atmojo, Leonardo Lossi, E. Alberti et al.
2 citations
Digital Transformation in Industry
0.70
article

Addressing Privacy and Security Challenges at the Industry 5.0 Human-Intensive and Highly Automated Factory Floor

Valeriy Vyatkin, Udayanto Dwi Atmojo, Leonardo Lossi, E. Alberti, Mikhail V. Kolesnikov
article en
2 citations

Abstract

With Industry 5.0, prioritizing worker well-being, the integration of human-related data into production processes poses challenges related to data privacy and compliance with regulations such as GDPR. This paper presents a comprehensive approach to anonymizing sensitive data collected from wearable devices. We propose a methodology that combines real-time processing and complex anonymization algorithms, ensuring end-to-end protection. We employ techniques such as normalization, hashing, and feature dropping. Our findings contribute to advancing privacy-preserving techniques in Industry 5.0 and underscore the importance of safeguarding user privacy in the era of advanced manufacturing.

Luleå University of Technology (SE), Nextworks (Italy) (IT), Aalto University (FI)
HORIZON EUROPE Framework Programme
Industry, innovation and infrastructure
Openalex Percentile: Top 23%
Digital Transformation in Industry
0.70
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.