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 editorsTerziyan, Vagan; Bilokon, Bohdan; Gavriushenko, Mariia

Parent publication5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023)

Parent publication editorsLongo, Francesco; Shen, Weiming; Padovano, Antonio

Conference:

  • International Conference on Industry 4.0 and Smart Manufacturing

Place and date of conferenceLisbon, Portugal22.-24.11.2023

Journal or seriesProcedia Computer Science

ISSN1877-0509

eISSN1877-0509

Publication year2024

Publication date20/03/2024

Number in series232

Pages range2201-2212

Number of pages in the book3277

PublisherElsevier

Publication countryNetherlands

Publication languageEnglish

DOIhttps://doi.org/10.1016/j.procs.2024.02.039

Publication open accessOpenly available

Publication channel open accessOpen 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


Keywordsmanufacturing engineeringintelligent systemsmachine learningdata protectionencryption

Free keywordssmart manufacturing; data privacy; privacy-preserving machine learning; quality metric; homeomorphic encryption


Contributing organizations


Ministry reportingYes

Reporting Year2024

Preliminary JUFO rating1


Last updated on 2024-13-05 at 18:26