Project description
Improving air traffic management through machine learning collaboration on private data sets
Air traffic management (ATM) can greatly benefit from cyber-secured exploitation of large private data sets belonging to different stakeholders. Currently, however, there is a reluctance to share sensitive data. The EU-funded AICHAIN project is proposing an innovative digital information management (DIM) concept that will help exploit those valuable private datasets. It will combine two emerging DIM technologies, federated machine learning and blockchain, to articulate an advanced privacy-preserving federated learning design in which exchanging data and training models will not be compromised. It will also investigate the potential benefits of the innovative DIM through ATM research cases related to the advanced demand capacity balancing prognostic model of the network manager augmented with real operational data from airspace users.
Objective
This project proposes an innovative Digital Information Management (DIM) concept, i.e. the AICHAIN solution, that aims at enabling the cyber-secured exploitation of large private data sets that belong to different stakeholders and that contain valuable information for ATM operations. To overcome the stakeholders’ reluctance to share sensitive data, the exploitation will not be performed by exchanging the data itself but by articulating an advanced privacy-preserving federated learning architecture in which neither the training data nor the training model need to be exposed. This will be possible thanks to the innovative combination of two emerging DIM technologies: Federated Machine Learning (FedML) and Blockchain technologies.
The potential benefits of the new proposed DIM concept will be explored through ATM research use cases related to advanced Demand Capacity Balancing (DCB) predictive models of the Network Manager (NM), whose prediction performance is expected to significantly improve thanks to the exploitation of relevant operational private data from Airspace Users. The accuracy of the new DCB predictive models augmented with real operational data accessed through the AICHAIN solution will be benchmarked against the machine learning models for DCB that are currently in use or under research by NM.
The project will also address the exploration of governance and incentives mechanisms as part of the AICHAIN solution concept architecture, to facilitate the adoption of the concept and the alignment of interests of the key stakeholders (especially of the data owners). The design of advanced governance & incentives mechanisms, which could be implemented using the mechanism of “smart contracts” available in the toolset of Blockchain, will be complemented with a theoretical identification of data exploitation benefits and with discussions in workshops participated by external experts.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- social sciences sociology governance
- natural sciences computer and information sciences computer security cryptography
- social sciences social geography transport transport planning air traffic management
- natural sciences computer and information sciences artificial intelligence machine learning
You need to log in or register to use this function
We are sorry... an unexpected error occurred during execution.
You need to be authenticated. Your session might have expired.
Thank you for your feedback. You will soon receive an email to confirm the submission. If you have selected to be notified about the reporting status, you will also be contacted when the reporting status will change.
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
-
H2020-EU.3.4. - SOCIETAL CHALLENGES - Smart, Green And Integrated Transport
MAIN PROGRAMME
See all projects funded under this programme -
H2020-EU.3.4.7. - SESAR JU
See all projects funded under this programme
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
RIA - Research and Innovation action
See all projects funded under this funding scheme
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-SESAR-2019-2
See all projects funded under this callCoordinator
Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
08005 Barcelona
Spain
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.