Privacy-Protected Machine Learning for Transport Systems
Forskningsprojekt, 2020
– 2021
This interdisciplinary project addresses forward-looking challenges in machine learning (ML) using prevailing and methodologies from the areas of computer security and distributed systems. Current ML implementations often need to collect a large amount of information that can be privacy-sensitive, before applying ML algorithms. It is a common practice to sanitize such privacy-sensitive information. However, there were numerous reports about cases in which by combining sanitized datasets turned out to violate privacy requirements. This project will seek to develop privacy-protected manipulation-resilient ML solutions. The project will provide imperative privacy-preserving analytics tools needed for working with ML in the automotive industry.
Deltagare
Elad Schiller (kontakt)
Chalmers, Data- och informationsteknik, Nätverk och system
Irene Yu-Hua Gu
Chalmers, Elektroteknik
Tomas Olovsson
Chalmers, Data- och informationsteknik, Nätverk och system
Shiliang Zhang
Chalmers, Data- och informationsteknik, Nätverk och system
Samarbetspartners
Chalmers AI-forskningscentrum (CHAIR)
Gothenburg, Sweden
Finansiering
AoA Transport Funds
Finansierar Chalmers deltagande under 2020–2021
Relaterade styrkeområden och infrastruktur
Transport
Styrkeområden