Duration: 2018 - 2020
Funded by:German Federal Ministry of Education and Research (grant numbers 01IS17106A and 01IS17106B), and Technological Agency of the Czech Republic (project no. 2017TF04000064)
Principal Investigators:  Robert Heinrich, Jiri Havlik
Members:Rima Al-Ali, Spiros Alexakis, Tomas Bures, Thomas Genssler, Jiri Havlik, Björn-Oliver Hartmann, Robert Heinrich, Petr Hnetynka, Adrian Juan-Verdejo, Pavel Parizek, Stephan Seifermann, Maximilian Walter
Organizations: CAS Software AG, Univerzita Karlova, Karlsruhe Institute of Technology, Institut mikroelektronických aplikací s.r.o.

Industry 4.0 enacts ad-hoc cooperation between machines, humans, and organizations in supply and production chains. The cooperation goes beyond rigid hierarchical process structures and increases the levels of efficiency, customization, and individualisation of end-products. Efficient processing and cooperation requires exploiting various sensor and process data and sharing them across various entities including computer systems, machines, mobile devices, humans, and organisations. Modern software-intensive Industry 4.0 systems process data with distributed and decentralized resources according to multiple organisational roles with different privileges to access and manipulate data both inside and across organisations. The ad-hoc horizontal cooperation in Industry 4.0 systems is a disruptive development that makes current privacy and trust mechanisms unsuitable as they are built around rigid hierarchical infrastructures. Trust 4.0 proposes a novel approach to privacy and trust tied to the dynamics of custom product engineering, hence, establishing privacy and trust in the ad-hoc horizontal processes. The image shows an exemplary supply chain involving different roles such as worker, manager, and customer across different organisations such as Factory 1-3. Within a factory, products are assembled from base material or sub-products using a number of devices jointly cooperating inside and across organisations. The sensors in these devices capture sensor data to send them to an IoT gateway for pre-processing and system entities identification—human or inanimate—so that the edge cloud stores the relevant data. The edge-cloud provides a privacy-aware data processing platform that makes data available only to those entities and processes allowed to see them. The edge cloud do on-site local processing so that highly sensitive data never have to leave the premises. In our use-case, a shop floor management system (SMS) runs in the privacy-aware edge cloud locally creating role-specific aggregations of data. The SMS is a virtual collaboration tool to control and monitor complex projects spanning across multiple shop floor locations. Although the different roles are involved in the same supply chain they are not allowed to look at the same data. Additionally, some data must be available to the global pool of factories and roles through the global SMS whereas other data cannot be shared. Consequently, there is a need for privacy-aware and trustful data sharing and coordination across the entire supply chain.