Imagine a digital vault where you own your ID and decide exactly who sees what, without giving away your master key. It's like having a smart assistant that can analyze your data or train AI models without ever actually seeing your private information. The system also acts like a security guard that automatically spots and stops digital break-ins.
Companies struggle to share sensitive data for AI training and identity verification without risking privacy leaks or violating EU laws. Current tools are often closed-source, expensive, or lack the scalability needed for industrial volumes.
An open-source platform featuring a core of identity and cryptography managers, value-added services like self-sovereign wallets and federated learning, and a security manager for cyber-attack detection.
If you are a digital payment provider dealing with strict KYC and identity theft — this project developed a self-sovereign wallet and identity lifecycle manager that gives users control over their credentials while ensuring regulatory compliance.
If you are a medical research consortium dealing with sensitive patient records — this project developed privacy-preserving federated learning and record linkage that allows training AI models across different hospitals without moving the actual data.
If you are an e-commerce platform dealing with GDPR and consumer data privacy — this project developed an anonymization manager and compliance manager for EU legislation that automates data protection rules.
Based on available project data, the platform is being developed as open-source, which typically implies no licensing fees for the core software, though specific implementation costs are not listed.
Yes, the project specifically aims to tweak methods to enhance time efficiency and scalability, and will be tested in 3 industrial use cases.
The project is explicitly described as an open-source platform, meaning the code is intended to be publicly available and reusable.
The platform includes a compliance manager specifically designed for horizontal EU legislation to ensure data sharing meets legal requirements.
The components are built as libraries that can be integrated into any system leveraging Python's open-source ecosystem.
The consortium is well-balanced for technology transfer, featuring a 40% industry ratio with 4 industrial partners (including 2 SMEs) and 5 universities across 8 countries. This mix ensures that the academic research from the coordinator (University of Athens) is grounded in practical industrial requirements.
Contact the National and Kapodistrian University of Athens
Contact us to find the specific Python libraries developed by RECITALS for your data privacy needs.
