SPHN/PHRT - MedCo in Action: Empowering the Swiss Molecular Tumor Board with Privacy-Preserving and Real-Time Patient Discovery

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Robust and Personalized Federated Learning: Handling Data Corruption and Heterogeneity

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Federated Learning (FL) has emerged as a transformative paradigm in machine learning, enabling collaborative model training across decentralized devices while preserving data privacy. However, FL's success is highly contingent on the quality and integrity ...

EPFL2025

Privacy and Confidentiality in Machine Learning and Data Analysis: Understanding Risks and Developing Protections

Valentin Hartmann

Without the ability to collect, access and analyze data, most of nowadays research would be impossible. Without data to learn from, the field of machine learning (ML) would not exist. However, much of the particularly useful data---medical records, human b ...

EPFL2024

Noiseless Privacy-Preserving Decentralized Learning

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Decentralized learning (DL) enables collaborative learning without a server and without training data leaving the users' devices. However, the models shared in DL can still be used to infer training data. Conventional defenses such as differential privacy ...

Privacy Enhancing Technologies Symposium Advisory Board2024

A Blockchain-Based Interoperable Architecture for IoT with Selective Disclosure of Information

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With the improvement of Internet of Things (IoT) technologies, services, and applications, there is a proliferation of access to smart devices in everyday life. However, granting access and controlling access rights for each resource is challenging in high ...

IEEE Computer Society2023