Category: Text Anonymization on Sensitive Data
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: 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 Video Series: Text Anonymization on Sensitive Data
The SIESTA Video Series brings together a set of short videos in which several of the project’s use cases present their work and main objectives. In particular, the use cases on Medical Imaging, Energy Domain, Text Anonymization on sensitive data, and Demography have contributed to this series, each explaining their respective activities within the project.…