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Digital transformation strengthens the interconnection of companies in order to develop optimized and better customized, cross-company business models. These models require secure, reliable, and traceable evidence and monitoring of contractually agreed information to gain trust between stakeholders. Blockchain technology using smart contracts allows the industry to establish trust and automate cross-company business processes without the risk of losing data control. A typical cross-company industry use case is equipment maintenance. Machine manufacturers and service providers offer maintenance for their machines and tools in order to achieve high availability at low costs. The aim of this chapter is to demonstrate how maintenance use cases are attempted by utilizing hyperledger fabric for building a chain of trust by hardened evidence logging of the maintenance process to achieve legal certainty. Contracts are digitized into smart contracts automating business that increase the security and mitigate the error-proneness of the business processes.
Blockchain frameworks enable the immutable storage of data. A still open practical question is the so called "oracle" problem, i.e. the way how real world data is actually transferred into and out of a blockchain while preserving its integrity. We present a case study that demonstrates how to use an existing industrial strength secure element for cryptographic software protection (Wibu CmDongle / the "dongle") to function as such a hardware-based oracle for the Hyperledger blockchain framework. Our scenario is that of a dentist having leased a 3D printer. This printer is initially supplied with an amount of x printing units. With each print action the local unit counter on the attached dongle is decreased and in parallel a unit counter is maintained in the Hyperledger-based blockchain. Once a threshold is met, the printer will stop working (by means of the cryptographically protected invocation of the local print method). The blockchain is configured in such a way that chaincode is executed to increase the units again automatically (and essentially trigger any payment processes). Once this has happened, the new unit counter value will be passed from the blockchain to the local dongle and thus allow for further execution of print jobs.
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 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.