Research Project
MUSKETEER: augmented shared knowledge thanks to Machine Learning
Our research project to build a validated, federated, privacy-preserving Machine Learning platform tested on industrial data.
Where: international
Where: international
Challenge
Industrial Data Platforms (IDPs) are still encountering several obstacles that limit their usage: legal barriers, data ownership, different privacy policies among actors, mistrust towards how data is managed.
Approach
Machine Learning is a disruptive technology in many sectors, as it provides a scalable and flexible way to adopt data-driven decision-making processes.IDPs meet the need to establish reliable networks where data can be transferred, accessed, and used securely. MUSKETEER leverages new paradigms in machine learning research, such as Federated Machine Learning, Privacy Preserving Machine Learning and protection against Adversarial Attacks.
Digital Ecosystem
Solution
Digital Ecosystem
MUSKETEER aims to develop an Industrial Data platform which follows actual industrial standards and offers scalable algorithms for federated and privacy-preserving machine learning techniques with detection and mitigation of adversarial attacks and a rewarding model capable to fairly monetize datasets according to the real data value.
In the project, Engineering makes use of its skill and knowledge to design and develop Federated Machine Learning client connectors for the scalable, secure, trusted and privacy aware communication with the server side.
The project has received co-funding from the European Union's Horizon 2020 programme - Contract No. 824988
Financing Framework
Results
Robustness against external and internal threats
Support to Data Economy development
Standardized and extensible architecture
Industrial demonstration in operational environment
Impacts
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