Imagine you have a secret recipe, but you want a chef in another country to improve it without ever actually seeing the ingredients. This technology lets different organizations share and analyze data while it stays locked in a digital vault. It uses a special kind of encryption that allows computers to work on the data without unlocking it first.
Organizations cannot share valuable data across borders because they lack common standards and fear privacy breaches or security leaks. This makes interdisciplinary research and business collaboration nearly impossible.
A secure data aggregation system featuring homomorphic encryption, a blockchain transaction recorder, and an AI playground for transparent machine learning workflows.
If you are a medical research consortium dealing with strict patient privacy laws across borders — this project developed a secure data ecosystem that allows researchers to analyze encrypted health records without exposing personal identities.
If you are a connected vehicle manufacturer dealing with fragmented sensor data from different countries — this project developed a way to search and process data in the encrypted domain to improve AI models without risking trade secrets.
If you are a smart grid operator dealing with sensitive energy consumption patterns — this project developed a blockchain-based recorder and encryption layer that ensures all data interactions are accountable and private.
Based on available project data, no specific commercial pricing or cost-per-license is mentioned; it was funded by an EU contribution of EUR 8,706,263.
Yes, the project is designed for Pan-European federation and is validated through 6 distinct use cases supporting large-scale initiatives like GAIA-X and EOSC.
Based on available project data, specific licensing terms are not provided, but the project emphasizes a FAIR (Findable, Accessible, Interoperable, Reusable) representation of data.
The system is built to ensure compliance with European privacy and ethical frameworks, specifically mentioning GDPR and Privacy Impact Assessments (PIA).
It aggregates interdisciplinary data repositories and uses advanced ontologies to allow complex queries across different data standards.
The consortium is heavily weighted toward commercial application, with 13 industry partners (59% ratio) and 7 SMEs. This strong industrial presence, combined with 8 academic and research entities across 13 countries, suggests the technology is being developed with a clear eye toward market adoption rather than just theoretical research.
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