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 ...
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 ...
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 ...
Side-channel attacks allow adversaries to infer sensitive information from non-functional characteristics. Prior side-channel detection work is able to identify numerous potential vulnerabilities. However, in practice, many such vulnerabilities leak a negl ...
MedCo is the first operational system that makes sensitive medical-data available for research in a simple, privacy-conscious and secure way. It enables a consortium of clinical sites to collectively protect their data and to securely share them with inves ...
