A4 Article in conference proceedings
Deep Homeomorphic Data Encryption for Privacy Preserving Machine Learning (2024)


Terziyan, V., Bilokon, B., & Gavriushenko, M. (2024). Deep Homeomorphic Data Encryption for Privacy Preserving Machine Learning. In F. Longo, W. Shen, & A. Padovano (Eds.), 5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023) (pp. 2201-2212). Elsevier. Procedia Computer Science, 232. https://doi.org/10.1016/j.procs.2024.02.039


JYU authors or editors


Publication details

All authors or editors: Terziyan, Vagan; Bilokon, Bohdan; Gavriushenko, Mariia

Parent publication: 5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023)

Parent publication editors: Longo, Francesco; Shen, Weiming; Padovano, Antonio

Conference:

  • International Conference on Industry 4.0 and Smart Manufacturing

Place and date of conference: Lisbon, Portugal, 22.-24.11.2023

Journal or series: Procedia Computer Science

ISSN: 1877-0509

eISSN: 1877-0509

Publication year: 2024

Publication date: 20/03/2024

Number in series: 232

Pages range: 2201-2212

Number of pages in the book: 3277

Publisher: Elsevier

Publication country: Netherlands

Publication language: English

DOI: https://doi.org/10.1016/j.procs.2024.02.039

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/94147


Abstract

Addressing privacy concerns is critical in smart manufacturing where sensitive data is used for machine learning. Data protection is essential to ensure model accuracy while upholding data privacy. Homeomorphic encryption, an algorithm for privacy-preserving machine learning, achieves this by transforming data using a neural network with secret key weights. This process conceals private data while retaining the potential to learn classification models from the anonymized data. This paper introduces a comprehensive quality metric to assess homeomorphic encryption across conflicting criteria: security (regarding private data), machine learning adaptability (tolerance), and efficiency (regarding needed extra resources). Through experiments on various datasets, the metric proves its effectiveness in guiding optimal encryption parameter selection. Our findings highlight homeomorphic encryption's strong overall quality, positioning it as a valuable Industry 4.0 solution. By offering a simpler alternative to fully homomorphic encryption, it effectively addresses privacy concerns and enhances data usability in the context of smart manufacturing.
See presentation slides: https://ai.it.jyu.fi/ISM-2023-Encryption_Metric.pptx


Keywords: manufacturing engineering; intelligent systems; machine learning; data protection; encryption

Free keywords: smart manufacturing; data privacy; privacy-preserving machine learning; quality metric; homeomorphic encryption


Contributing organizations


Ministry reporting: Yes

VIRTA submission year: 2024

Preliminary JUFO rating: 1


Last updated on 2024-03-07 at 01:07