Tag: Secure Data Sharing
SIESTA Concepts #4 | FAIR Data
EOSC-SIESTA bridges the gap between open science and data protection by applying FAIR principles to sensitive data. Through trusted cloud environments, the project demonstrates that data can be secure, confidential, and controlled while remaining findable, accessible, interoperable, and reusable for scientific research.
New EOSC-SIESTA publication: privacy-preserving Federated Learning in medical imaging
A new EOSC-SIESTA publication by IFCA-CSIC, INRIA, and TU Delft explores privacy protection in Federated Learning using sensitive medical imaging data. The study proposes a metric-privacy-inspired noise calibration strategy that improves global model accuracy while defending against Client Inference Attacks.
Discover UC3 Energy Domain Tool: optimising solar battery management in EOSC-SIESTA
EOSC-SIESTA addresses the challenge of analyzing sensitive data without compromising privacy through its energy domain use case (UC3). Focusing on solar installations, the project utilizes trusted execution environments and anonymization techniques to secure sensitive household consumption dat
Discover pyCANON: assessing dataset anonymity in EOSC-SIESTA
pyCANON is an open-source Python library and web application developed within EOSC-SIESTA to assess the anonymity and privacy risks of tabular datasets. Integrated into the project’s dashboard, it evaluates data against key anonymity models like k-anonymity and l-diversity, enabling researchers to make informed decisions before sharing sensitive information.