Tag: Sensitive Data Analytics
SIESTA Concepts #5: Named Entity Recognition
Sharing cyber incident reports is vital for threat detection, but they’re full of personal data. Anonymisation pipelines rely on Named Entity Recognition models to catch sensitive mentions first — and these are usually trained in English. Researchers within EOSC SIESTA tested whether that holds up in Spanish, and found that multilingual models outperform English cybersecurity…
SIESTA tool: DatLeak
EOSC-SIESTA (Secure Interactive Environments for SensiTive data Analytics) is a European project, funded under the Horizon Europe programme, that strengthens the European Open Science Cloud (EOSC) with trusted, cloud-based environments for managing and analysing sensitive data. Its goal is to give researchers practical, reproducible ways of working with confidential datasets while still respecting FAIR principles,…
SIESTA tool: BIDScramble
BIDScramble is an open-source tool developed by Radboud University within the EOSC-SIESTA project to facilitate the secure reuse of sensitive neuroimaging data. It transforms BIDS-formatted datasets into anonymous scrambled versions that maintain original structural and statistical properties.
SIESTA Tool: Dashboard
The EOSC-SIESTA Dashboard is the main interface connecting the project’s technical infrastructure with its practical research use cases. Based on Onyxia, this unified platform features a modular service catalog, MinIO S3-compatible storage, and a secure secret-management system, allowing researchers to safely access and analyze sensitive datasets.
3rd EOSC-SIESTA webinar: A journey through the SIESTA computing infrastructure
Discover the EOSC-SIESTA computing infrastructure on June 3, 2026. This third webinar offers a technical overview from the perspectives of providers, users, and developers, focusing on secure cloud environments, confidential computing, and sensitive data analytics.
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.
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
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.