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In recent years, Artificial Intelligence (AI) becomes ubiquitous and is leading to a technological paradigm shift. This trend is driven through an increasing hyperconnectivity based on the integration of distributed systems into the Internet infrastructure mainly based on the deployment of Internet of Things (IoT) technologies and 5G/6G infrastructures.  The integration of such systems will enable new data-based services, for example, in the context of sustainable cities and communities, or advanced eHealth services. To provide these services effectively and efficiently, a key aspect is the management of security and privacy throughout the data’s lifecycle in a way that ensures the services are based on trustworthy information provided by legitimate systems. In this direction, REMINDER will design a decentralized and secure approach for the access and processing of data produced by distributed systems. In particular, REMINDER will design and implement an edge-based architecture for applications using Federated Learning (FL) that will be accessible to resource-constrained systems. Unlike most current deployments, the architecture will enable a collaborative model creation without the need to share the data itself. This architecture will consider the dynamism of decentralized and distributed systems by designing a node selection approach for the training process in the FL architecture while considering end systems’ features and their evolution during their life cycle. Additionally, REMINDER will address some of the major security and privacy challenges associated with the use of decentralized Machine Learning (ML) approaches, such as FL by analysing the use of cryptographic techniques, such as Differential Privacy (DP) and Secure Multi-Party Computation (SMPC) for the sake of reaching the right balance between the effectiveness provided by ML techniques and the privacy being guaranteed. Data privacy will be considered in rest, transit, and while processing. The proposed solutions will be preventive and reactive, and they will also ensure the privacy preserving aspects are being compliant with existing data protection regulations, such as the GDPR over the data lifecycle. REMINDER will also design and implement an authentication protocol to ensure that only legitimate systems take part in the collaborative creation process of ML models. In addition, REMINDER will demonstrate the feasibility of the proposed research through two main use cases around eHealth and smart buildings. 

Call Topic: Security and Privacy in Decentralised and Distributed Systems (SPiDDS), Call 2022
Start date: (36 months)
Funding support: 1048526 €

Project partners

  • Universidad de Murcia - Spain (coordinator)
  • Austrian Institute of Technology GmbH - Austria
  • SC SIEMENS SRL - Romania
  • University of the West of England - United Kingdom