Information Systems Engineering

TEADAL: Trustworthy, Energy-Aware Federated Data Lakes Along the Computing Continuum

Modern applications heavily rely on data-driven processes – autonomous driving, for example, requires vast amounts of sensor and video data to train algorithms, and applications in intelligent production lines need a constant and timely flow of sensor data to identify production errors quickly. However, this data has long since ceased to be part of a single processing chain and is often generated and managed by various organizationally separate stakeholders. Accordingly, data sharing is of high importance for data-driven applications. However, when sharing such sensors, simulations, transactions, and data, a variety of business, technical and organizational issues arises regarding system performance, privacy, trustworthiness, and availability. The so-called Cloud-Edge Continuum, which enables the operation and management of data both close to the sensors as the data sources (the edge) and in remote cloud data lakes in the backend, provides initial answers to address availability and performance issues. Thus, the question remains on how to ensure the confidentiality and trustworthiness of data beyond the organizational context through cloud-edge continuum applications.

Together with several renowned international partners, we will address this issue in the Horizon Europe project "TEADAL" (Trustworthy, Energy-Aware Federated Data Lakes Along the Computing Continuum) by developing a novel cloud and edge data management system that shares data sets in a traceable, trustworthy and confidential manner. At the same time, this new system will place emphasis on energy efficiency, especially considering the environmental footprint of shared data systems. In digital agriculture, for example, it should be possible to create models for predicting and combating plant diseases based on observations from farms distributed across Europe, some of which compete with each other, without revealing confidential farming data. Thus, all farms can benefit from joint data collection and model development without disclosing company secrets.

A major scientific challenge lies in the heterogeneity of these data sources and owners. A system that ensures trustworthiness and confidentiality must work in a traceable and transparent manner while at the same time not disclosing data directly. Therefore, properly selecting and using software-based solutions for distributed confidential computing and dynamic data modification is indispensable. Thus, our focus in the project lies on approaches in the areas of blockchain-based systems, off-chaining, zero-knowledge proofs, secure multi-party computation, trusted execution environments (TEEs), rollups as well as (runtime-) observability, energy tracing and privacy engineering.

Based on the fundamental research results of the academic partners TU Berlin, Politecnico Milano (Italy), and TU Wien (Austria) and contributions of the industry partners Ubiwhere (Portugal), Cybernetica (Estoniua),Cefriel (Italy),IBM Israel (Israel), Marina Salud Sa (Spain), UITP (Belgium),AMTS Catania (Italy),Almaviva (Italy), Martel GmbH (Switzerland) , Terraview GmbH (Switzerland), Ert Têxtil (Portugal),I2cat (Spain), Box2m Engineering (Romaina) and Regione Toscana (Italy) a distributed data management system in the fields of medicine, Industry 4.0, finance, telecommunications, digital agriculture, and regional planning will be evaluated, implemented, and tested. The project starts on Sep. 1, 2022 and lasts 3 years with a total funding volume of 10 million euros.


TEADAL Website


Principal Investigators

Sebastian Werner

Stefan Tai



Funded by the European Union (TEADAL, 101070186). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.