Privacy-Protected Machine Learning for Transport Systems
Research Project , 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.

Participants

Elad Schiller (contact)

Professor at Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Irene Yu-Hua Gu

Full Professor at Chalmers, Electrical Engineering

Tomas Olovsson

Head of Division at Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Shiliang Zhang

Post doc at Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Collaborations

Chalmers AI Research Centre

Gothenburg, Sweden

Funding

AoA Transport Funds

Funding Chalmers participation during 2020–2021

Related Areas of Advance and Infrastructure

Transport

Areas of Advance

Publications

More information

Latest update

2020-12-12