Algorithms and solutions for privacy-preserving data exchange in a multitenant network environment
Most of the use cases being considered for 6G networks will rely heavily on data processing. While data integrity is ensured by, for example, encryption techniques, there is still a lot of work to be done to preserve privacy when data is processed between different tenants. This is a common case for many solutions that will be built on top of 6G, but also that are natively integrated into the network architecture, such as data exchanges between network service providers and network operators.
6G-RIEMANN will provide new solutions for: (i) Preserving privacy in data exchange between different tenants of 6G networks, (ii) Preserving privacy in analysis tasks through machine learning in a distributed manner among tenants, (iii) Implementation and integration of such solutions within state-of-the-art network management tools.
Marco Gramaglia
University Carlos III of Madrid (UC3M)
Marco Gramaglia is a visiting professor in the Department of Telematics Engineering at UC3M.
Before joining UC3M, Marco held postdoctoral research positions at the Istituto Superiore Mario Boella, the Institute of Electronic, Informatics and Telecommunication Engineering (IEIIT) of the Italian National Research Council (CNR) and at the IMDEA Networks institute. Marco obtained his PhD from UC3M in 2012, and has participated in many research projects such as H2020 DAEMON, H2020 5G-TOURS and H2020 5G-MoNArch.
Subprojects
6G-RIEMANN-DS
This project will investigate the exchange of data between tenants in a secure manner and with privacy guarantees.
6G-RIEMANN-ML
This project will investigate new distributed machine learning tools with guaranteed privacy between tenants.
6G-RIEMANN-SI
This project will introduce privacy concepts in data exchange in the context of tools already operating in network monitoring and visualization systems.
6G-RIEMANN-FR
This project will create an ecosystem with privacy guarantees between tenants, integrating it into state-of-the-art network architectures.
