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Verifiable Machine Learning Models in Industrial IoT via Blockchain

  • The importance of machine learning (ML) has been increasing dramatically for years. From assistance systems to production optimisation to healthcare support, almost every area of daily life and industry is coming into contact with machine learning. Besides all the benefits ML brings, the lack of transparency and difficulty in creating traceability pose major risks. While solutions exist to makeThe importance of machine learning (ML) has been increasing dramatically for years. From assistance systems to production optimisation to healthcare support, almost every area of daily life and industry is coming into contact with machine learning. Besides all the benefits ML brings, the lack of transparency and difficulty in creating traceability pose major risks. While solutions exist to make the training of machine learning models more transparent, traceability is still a major challenge. Ensuring the identity of a model is another challenge, as unnoticed modification of a model is also a danger when using ML. This paper proposes to create an ML Birth Certificate and ML Family Tree secured by blockchain technology. Important information about training and changes to the model through retraining can be stored in a blockchain and accessed by any user to create more security and traceability about an ML model.show moreshow less

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Metadaten
Document Type:Conference Proceeding
Conference Type:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/8389
Bibliografische Angaben
Title (English):Verifiable Machine Learning Models in Industrial IoT via Blockchain
Conference:International Advanced Computing Conference (12. : December 16-17, 2022 : Hyderabad, India)
Author:Jan Stodt, Fatemeh Ghovanlooy GhajarStaff MemberORCiDGND, Christoph Reich, Nathan Clarke
Edition:1.
Year of Publication:2023
Place of publication:Cham
Publisher:Springer
First Page:66
Last Page:84
Parent Title (English):Advanced Computing : 12th International Conference, IACC 2022, Hyderabad, India, December 16–17, 2022, Revised Selected Papers, Part II
Editor:Deepak Garg, V. A. Narayana, P. N. Suganthan, Jaume Anguera, Vijaya Kumar Koppula, Suneet Kumar Gupta
Volume:CCIS 1782
ISBN:978-3-031-35643-8 (Softcover)
ISBN:978-3-031-35644-5 (eBook)
ISSN:1865-0929
ISSN:1865-0937 (E-ISSN)
DOI:https://doi.org/10.1007/978-3-031-35644-5_6
Language:English
Inhaltliche Informationen
Institutes:Forschung / ivESK - Institut für verlässliche Embedded Systems und Kommunikationselektronik
Institutes:Bibliografie
Tag:Blockchain; Cybersecurity; Machine learning; Poisoning; Verifiability
Formale Angaben
Relevance:Konferenzbeitrag: h5-Index < 30
Open Access: Closed 
Licence (German):License LogoUrheberrechtlich geschützt