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)

Biträdande professor vid Chalmers, Data- och informationsteknik, Nätverk och system

Irene Yu-Hua Gu

Professor vid Chalmers, Elektroteknik

Tomas Olovsson

Docent vid Chalmers, Data- och informationsteknik, Nätverk och system

Shiliang Zhang

Doktor vid Chalmers, Data- och informationsteknik, Nätverk och system

Samarbetspartners

Chalmers AI Research Centre

Gothenburg, Sweden

Finansiering

AoA Transport Funds

Finansierar Chalmers deltagande under 2020–2021

Relaterade styrkeområden och infrastruktur

Transport

Styrkeområden

Publikationer

Mer information

Senast uppdaterat

2020-12-12