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.


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


Chalmers AI Research Centre

Gothenburg, Sweden


AoA Transport Funds

Finansierar Chalmers deltagande under 2020–2021

Relaterade styrkeområden och infrastruktur




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