The purpose of this grant is to explore, design and implement protocols that conciliate privacy and confidentiality guarantees to handle increasingly more input data sources, both from edge or cloud environments, with the extraction of valuable information from the data that can be trusted, an issue exacerbated considering multiple data owners or across administrative domains. In detail, the protocols should be able to work with anonymized data, as well as provide secure data exchange between the different processing elements in distinct environments, centralized (e.g. cloud) towards the edge. Specifically, if anonymization is not applicable, data encryption may be an alternative, e.g. taking advantage of trusted execution environments and related technologies. This includes the exploration of emergent federated machine learning techniques that will reinforce the analytic capabilities.
MSc Degree in Informatics Engineering
Minimum profile required
- Solid knowledge on distributed systems and edge computing.- Solid knowledge in security- Solid knowledge in systems benchmarking
- Experience in secure data storage and processing systems
Since 26 Jun 2020 to 09 Jul 2020
Cluster / Centre
Computer Science / High-Assurance Software