Trustworthy Data Ecosystems

The integration and sharing of data across organizational boundaries poses big challenges for the secure storage and processing of data. Addressing those challenges requires new trusted paradigms and mechanisms for data management and processing. Today, data sharing is typically enabled by centralized solutions by big internet companies. This entails high risks because they require unlimited trust in a central authority and represent a “single point of failure”. Decentralized solutions can offer significant advantages in terms of security and resilience.

The goal of this research area is to create a solid technological basis for data management of shared data to enable secure and trustworthy processing across organizational boundaries by implementing a decentralized platform where different actors can jointly use and process trustworthy data without one party having to trust the other or disclose data to a central party. In addition to this fundamentally decentralized approach, participants will also be able to leverage additional centralized resources for processing shared data. The resulting hybrid data architecture will allow a trustworthy, reliable and self-determined data sharing which is relevant for many different application areas such as health, production and finance. The research questions addressed in TRUDATA focus on fundamental aspects and technical challenges for trustworthy data ecosystems and cover a wide range of areas from cybersecurity, to secure hardware and secure protocols for data management and machine learning.