The C4DT Agency facilitates the identification and setup of projects between the partners and EPFL laboratories, to accelerate the deployment of cutting-edge commercially viable solutions. You can find a subset of such projects below, as well as a call for fellowships.

Latest Projects

Mar, 2022 → Nov, 2022
Partner: armasuisse
Partner contact: Alain Mermoud
EPFL laboratory: Distributed Information Systems Laboratory (LSIR)
EPFL contact: Prof. Karl Aberer, Angelika Romanou

The objective of the TMM project is to identify, at an early stage, the risks associated with new technologies and develop solutions to ward off such threats. It also aims to assess existing products and applications to pinpoint vulnerabilities. In that process, artificial intelligence and machine learning will play an important part. The main goal of this project is to automatically identify technology offerings of Swiss companies especially in the cyber security domain. This also includes identifying key stakeholders in these companies, possible patents, published scientific papers.

Jan, 2022 → Dec, 2023
Partner: Microsoft
Partner contact: Adrien Ghosn, Marios Kogias
EPFL laboratory: Data Center Systems Laboratory (DCSL) , HexHive Laboratory
EPFL contact: Prof. Edouard Bugnion, Prof. Mathias Payer

Confidential computing is an increasingly popular means to wider Cloud adoption. By offering confidential virtual machines and enclaves, Cloud service providers now host organizations, such as banks and hospitals, that abide by stringent legal requirement with regards to their client’s data confidentiality. Unfortunately, confidential computing solutions depend on bleeding-edge emerging hardware that (1) takes long to roll out at the Cloud scale and (2) as a recent technology, it is bound to frequent changes and potential security vulnerabilities. This proposal leverage existing commodity hardware combined with new programming language and formal method techniques and identify how to provide similar or even more elaborate confidentiality and integrity guarantees than the existing confidential hardware.

Jan, 2022 → Dec, 2023
Partner: Microsoft
Partner contact: Dimitrios Dimitriadis, Emre Kıcıman, Robert Sim, Shruti Tople
EPFL laboratory: Data Science Lab (dlab)
EPFL contact: Prof. Robert West, Valentin Hartmann, Maxime Peyrard

As machine learning (ML) models are becoming more complex, there has been a growing interest in making use of decentrally generated data (e.g., from smartphones) and in pooling data from many actors. At the same time, however, privacy concerns about organizations collecting data have risen. As an additional challenge, decentrally generated data is often highly heterogeneous, thus breaking assumptions needed by standard ML models. Here, we propose to “kill two birds with one stone” by developing Invariant Federated Learning, a framework for training ML models without directly collecting data, while not only being robust to, but even benefiting from, heterogeneous data.

C4DT Digital Trust Fellowship Program

The Center for Digital Trust (C4DT) digital trust policy fellowship program supports scholars and practitioners working at the intersection of trust-building tech and public policy to identify, analyze, and respond to critical issues concerning digital trust, e.g., privacy protection, cyber security, artificial intelligence (AI) & machine learning (ML), digital ledgers, big data, or the internet of things (IoT).