Privacy-Preserving Technologies for Trusted Data Spaces

Abstract

The quality of a machine learning model depends on the volume of data used during the training process. To prevent low accuracy models, one needs to generate more training data or add external data sources of the same kind. If the first option is not feasible, the second one requires the adoption of a federated learning approach, where different devices can collaboratively learn a shared prediction model. However, access to data can be hindered by privacy restrictions. Training machine learning algorithms using data collected from different data providers while mitigating privacy concerns is a challenging problem. In this chapter, we first introduce the general approach of federated machine learning and the H2020 MUSKETEER project, which aims to create a federated, privacy-preserving machine learning Industrial Data Platform. Then, we describe the Privacy Operations Modes designed in MUSKETEER as an answer for more privacy before looking at the platform and its operation using these different Privacy Operations Modes. We eventually present an efficiency assessment of the federated approach using the MUSKETEER platform. This chapter concludes with the description of a real use case of MUSKETEER in the manufacturing domain.