@inproceedings{GhovanlooyGhajarSikoraStodtetal.2022,
  author    = {Fatemeh Ghovanlooy Ghajar and Axel Sikora and Jan Stodt and Christoph Reich},
  title     = {Machine Learning Models in Industrial Blockchain, Attacks and Contribution},
  series = {The Upper-Rhine Artificial Intelligence Symposium (UR-AI 2022) : AI Applications in Medicine and Manufacturing},
  editor    = {Christoph Reich and Ulrich Mescheder},
  isbn      = {978-3-00-073638-4 (e-ISBN)},
  pages     = {106 -- 111},
  year      = {2022},
  abstract  = {The importance of machine learning has been increasing dramatically for years. From assistance systems to production optimisation to support the health sector, almost every area of daily life and industry comes into contact with machine learning. Besides all the benefits that ML brings, the lack of transparency and the difficulty in creating traceability pose major risks. While there are solutions that make the training of machine learning models more transparent, traceability is still a major challenge. Ensuring the identity of a model is another challenge. Unnoticed modification of a model is also a danger when using ML. One solution is to create an ML birth certificate and an 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.},
  language  = {en}
}