SUPR
ML/AI for securing federated learning-based intelligent transportation systems
Dnr:

NAISS 2025/22-1113

Type:

NAISS Small Compute

Principal Investigator:

Sheng Liu

Affiliation:

Kungliga Tekniska högskolan

Start Date:

2025-09-01

End Date:

2026-09-01

Primary Classification:

10202: Information Systems (Social aspects at 50804)

Webpage:

Allocation

Abstract

The topic of the work is in the broad area of networked system security, with a particular focus on secure and privacy-preserving approaches and technologies for autonomous systems enabled by wireless and mobile communications, notably intelligent transportation systems. The focus will be on solutions that combine cyber and physical properties and focus on data to secure coordination and automation, with vehicles as a main use case. The work shall develop, with emphasis on machine learning and artificial intelligence, algorithms, protocols, and mechanisms, analyze their security and privacy and effect on safety, implement and evaluate their performance, and its effect on that of the encompassing system. Different systems will be considered, notably intelligent, connected and autonomous vehicles, as well as embedded or mobile participatory sensing systems.