Imagine you want to study a rare disease but the patient data is locked in different hospitals across Europe for privacy reasons. Instead of moving the data, this tool lets the AI 'visit' the data and learn from it without ever seeing the actual private details. It's like reading a book through a frosted window where you get the main ideas but can't see the specific names.
Health data is often siloed due to privacy laws and security risks, preventing AI developers from accessing the large, unbiased datasets needed for accurate medical tools.
The SECURED Innohub, a platform providing tools for anonymization, synthetic data generation, and secure multi-party computation for health data.
If you are a software developer dealing with the lack of diverse training data for tumor classification — this project developed the SECURED Innohub that allows training AI on decentralized hospital data without compromising patient privacy.
If you are a provider dealing with strict privacy laws when monitoring children's health — this project developed anonymization and synthetic data tools that allow you to share and analyze data across borders safely.
If you are a lab dealing with the high risk of leaking sensitive genomic sequences — this project developed Secure Multi-Party Computation that lets multiple parties compute results without any one party seeing the raw input.
Based on available project data, no specific pricing or cost structure is mentioned; the project focuses on providing tools and services to health technology SMEs.
Yes, the project specifically aims to scale up multiparty computation and anonymization by improving algorithmic efficiency and hardware/software implementation to reduce overheads.
Based on available project data, specific licensing terms are not provided, but the project provides direct support to health technology SMEs through a funding call.
The SECURED Innohub is designed to connect EU health data hubs, researchers, and innovators into a decentralized collaboration ecosystem.
The project runs from 2023-01-01 to 2025-12-31, with validation and use case demonstrations occurring in the second half of the project.
The consortium is well-balanced for technology transfer, consisting of 18 partners across 9 countries. With a 28% industry ratio (including 5 industry partners and 2 SMEs), there is a strong link between the 7 universities and 5 research centers and the actual commercial market, ensuring the tools are built for practical business use.
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