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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.
The overview of public key infrastructure based security approaches for vehicular communications
(2015)
Modern transport infrastructure becomes a full member of globally connected network. Leading vehicle manufacturers have already triggered development process, output of which will open a new horizon of possibilities for consumers and developers by providing a new communication entity - a car, thus enabling Car2X communications. Nevertheless some of available systems already provide certain possibilities for vehicles to communicate, most of them are considered not sufficiently secured. During last 15 years a number of big research projects funded by European Union and USA governments were started and concluded after which a set of standards were published prescribing a common architecture for Car2X and vehicles onboard communications. This work concentrates on combining inner and outer vehicular communications together with a use of Public Key Infrastructure (PKI).