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Machine learning to augment shared knowledge in federated privacy-preserving scenarios

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

The massive increase in data collected and stored worldwide calls for new ways to preserve privacy while still allowing data sharing in respect of their sovereignty among multiple data owners.

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.
The work carried out during the reporting period has been consistent with the schedule. During RP1 we had two milestones:

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.
The first prototype of the Musketeer platform offers State-of-the-Art integration of privacy preserving techniques enabling participants of a data sharing ecosystem to exchange data in secure way. The project also showed promising results in the domain of data poisoning with the robust aggregation technique developed in some of our activities to defend against poisoning attacks and faulty clients compared to other state-of-the-art robust aggregation method. The new period will see more scientific achievements as more results will come with the project progress such as the current activity of some of the partners of the project about the applicability of Federated Learning to answer some GDPR challenges. This can have an important impact on how this technology could help the very contemporary issue of privacy.
MUSKETEER topology - M18