Tag: FAIR principles
EOSC-SIESTA Participates in the FIDELIS Support Adoption of Solutions
EOSC-SIESTA joins the EOSC-FIDELIS “Support for the Adoption of Solutions” training programme, offering tools for privacy-preserving data release. Selected teams will receive €7,500 in funding to participate in workshops between September and November 2026. Applications close June 30, 2026.
SIESTA Tool: Text Anonymization on Sensitive Data
EOSC-SIESTA has developed a text anonymization tool prototype to securely share sensitive cyber incident reports. Created by Universidad de León, the tool uses AI and four anonymization techniques to mask confidential data, enabling safe data reuse for research, machine learning, and collaborative cyber defence.
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.
Siesta Concepts: Open Science
Open Science promotes a transparent and collaborative research ecosystem, driven by Europe’s EOSC initiative. However, sharing sensitive data requires balancing openness with privacy. EOSC-SIESTA addresses this challenge by developing trusted, cloud-based environments and privacy-preserving tools that make sensitive data securely shareable and usable under FAIR principles.
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.
Siesta Concepts #2: Anonymization
Data anonymization transforms sensitive datasets to prevent individual identification while keeping data useful. Within EOSC-SIESTA, two open-source tools help researchers sanitize quasi-identifiers and assess privacy risks by applying advanced anonymity models (like k-anonymity and l-diversity) to protect sensitive tabular data.
SIESTA CONCEPTS#1: Federated Learning
Federated Learning (FL) allows multiple participants to collaboratively train AI models by keeping data local and sharing only encrypted model updates. Through the SIESTA dashboard, users can easily deploy secure FL clients, which connect to a central coordinating server hosted on AI4EOSC via the Flower framework and secure gRPC protocols, enabling privacy-preserving collaboration.