Privacy-Protected Machine Learning for Transport Systems
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.
- Chalmers AI Research Centre (Forskningsinstitut, Sweden)
Denna sidan finns endast på svenska
- AoA Transport Funds (Akademisk, Sweden)