Data trust (DT) is a term that has become increasingly established in the field of secure and non-profit-oriented data exchange in recent years. On the other hand, there is an enormous field of research into secure machine learning (SML), which greatly increases the data protection of local data or reduces the amount of data to be transmitted through compression.
Topics/use cases:
- Analysis of integration possibilities of DT with secure data exchange and SML
- Analysis for the implementation of a secure training environment within the company and/or across companies regarding data security/ zero trust and network traffic reduction
- Benchmarking of various SML and federated learning frameworks about compression, data security, user-friendliness, expandability in Industry 4.0
- Benchmarking of various training and federated learning algorithms from the literature using the example of anomaly detection in Industry 4.0
- Work on secure data pre-processing techniques for e.g. scaling the data and benchmarking the results
- More topics upon request