Responsible data science


Data science needs responsibility by design

AI technologies necessitate massive sharing of data to reach high performances, raising concerns about privacy issues and leaks of information. New technical and organizational solutions are therefore needed to build up trust and enable large scale collaborations of citizens, companies and institutions.


Various privacy-enhancing technologies have recently emerged from a community of innovators and academic researchers. Substra framework enables their secure and transparent combination to provide efficient privacy-preserving workflows for data science.

Substra framework provides


Substra framework enables working on private datasets without leaking personal information.


An immutable audit trail registers all the operations realized on the platform.


Substra framework makes it possible to shape new scientific and economic collaborations in data science.


 Technological features

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Data locality

Data remain in their owner's data stores and are never transferred. AI models travel from one dataset to another.

Decentralized trust

All operations are orchestrated by a distributed ledger technology, also called permissioned blockchain. There is no need for a single trusted actor: security arises from the network.


Substra framework is highly tunable: various permission regimes and workfow structures can be enforced corresponding to every use case specificities.


 Ongoing projects and consortiums

AI on clinical data
The HealthChain consortium gathers French hospitals, research centers and start-ups together with the Substra Foundation to develop AI models on clinical data. The training and validation of AI models are realized through Substra framework in order to secure remote analysis of sensitive data. This project will provide the first proof of concept of Substra framework and will prove its compliance with GDPR.

(9 partners, 10M€ funding)
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Drug discovery
The Melloddy project aims to develop a platform for creating more accurate models to predict which chemical compounds could be promising in the later stages of drug discovery and development. It demonstrates a new model of collaboration between traditional competitors in drug discovery and involves an unprecedented volume of competitive data. The platform being developed addresses the need for security and privacy preservation while allowing for enough information exchange to boost predictive performance.


(17 partners, 18M€ funding)
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