Periodic Reporting for period 1 - MUSKETEER (Machine learning to augment shared knowledge in federated privacy-preserving scenarios)
Reporting period: 2018-12-01 to 2020-05-31
Today, the lack of usable trusted and secure environments for data sharing inhibits data economy while legality, privacy, trustworthiness, data value and confidentiality hamper the free flow of data.
MUSKETEER aims to create a validated, federated, privacy-preserving machine learning Industrial Data Platform (IDP) that is inter-operable, scalable and efficient enough to be deployed in real use cases.
MUSKETEER aims to alleviate data sharing barriers by providing secure, scalable and privacy-preserving analytics over decentralized datasets using machine learning based on IDSA concepts (architecture model and components). An initial set of privacy preserving machine learning algorithms to solve regression, classification and clustering problems will be provided, although the platform will be flexible enough to accept new algorithmic implementations.
Data can continue to be stored in different locations with different privacy constraints, but shared securely. The MUSKETEER cross-domain platform will validate progress in the industrial scenarios of smart manufacturing and health.
MUSKETEER pursues different objectives:
1. Machine Learning over a high variety of different privacy-preserving scenarios.
2. Providing robustness against external and internal threats.
3. Enhancement of the Data Economy.
4. Providing a standardized and extensible architecture.
5. Industrial demonstration of the technology advances in operational environments.
MS1 Industrial, technical and legal requirements for the MUSKETEER platform.
MS2 Architecture final design of the MUSKETEER platform.
The achievement of these two milestones contributed to the development of a first prototype of the MUSKETEER platform which is able to host a wide variety of Machine Learning algorithms over a high variety of different privacy-preserving scenarios (from POM1 to POM6). The implementation of this initial prototype has been done after a detailed analysis of the appropriate design that can cater for all the different technical and end users’ requirements. A careful analysis in compliance with the legal and confidentiality restrictions of most industrial scenarios has also been conducted.