A4 Article in conference proceedings
Encryption and Generation of Images for Privacy-Preserving Machine Learning in Smart Manufacturing (2023)


Terziyan, V., Malyk, D., Golovianko, M., & Branytskyi, V. (2023). Encryption and Generation of Images for Privacy-Preserving Machine Learning in Smart Manufacturing. In F. Longo, M. Affenzeller, A. Padovano, & S. Weiming (Eds.), 4th International Conference on Industry 4.0 and Smart Manufacturing (217, pp. 91-101). Elsevier. Procedia Computer Science. https://doi.org/10.1016/j.procs.2022.12.205


JYU authors or editors


Publication details

All authors or editorsTerziyan, Vagan; Malyk, Diana; Golovianko, Mariia; Branytskyi, Vladyslav

Parent publication4th International Conference on Industry 4.0 and Smart Manufacturing

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

Conference:

  • International Conference on Industry 4.0 and Smart Manufacturing

Place and date of conferenceLinz, Austria2.-4.11.2022

Journal or seriesProcedia Computer Science

eISSN1877-0509

Publication year2023

Volume217

Pages range91-101

Number of pages in the book1954

PublisherElsevier

Publication countryNetherlands

Publication languageEnglish

DOIhttps://doi.org/10.1016/j.procs.2022.12.205

Publication open accessOpenly available

Publication channel open accessOpen Access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/85094


Abstract

Current advances in machine (deep) learning and the exponential growth of data collected by and shared between smart manufacturing processes give a unique opportunity to get extra value from that data. The use of public machine learning services actualizes the issue of data privacy. Ordinary encryption protects the data but could make it useless for the machine learning objectives. Therefore, “privacy of data vs. value from data” is the major dilemma within the privacy preserving machine learning activity. Special encryption techniques or synthetic data generation are being in focus to address the issue. In this paper, we discuss a complex hybrid protection algorithm, which assumes sequential use of two components: homeomorphic data space transformation and synthetic data generation. Special attention is given to the privacy of image data. Specifics of image representation require special approaches towards encryption and synthetic image generation. We suggest use of (convolutional, variational) autoencoders and pre-trained feature extractors to enable applying privacy protection algorithms on top of the latent feature vectors captured from the images, and we updated the hybrid algorithms composed of homeomorphic transformation-as-encryption plus synthetic image generation accordingly. We show that an encrypted image can be reconstructed (by the pre-trained Decoder component of the convolutional variational autoencoder) into a secured representation from the extracted (by either the Encoder or a feature extractor) and encrypted (homeomorphic transformation of the latent space) feature vector.
See presentation slides: https://ai.it.jyu.fi/ISM-2022-Image_Encryption.pptx


Keywordsindustrymanufacturing engineeringmachine learningcomputer visiondata protectionanonymityencryption

Free keywordsIndustry 4.0; data privacy; anonymization; syntetic data generation; image processing; autoencoders


Contributing organizations


Ministry reportingYes

Reporting Year2023

JUFO rating1


Last updated on 2024-15-05 at 13:02