Secure
Interactive Environments for SensiTive data Analytics

Effective and secure data processing within the European Open Science Cloud

Secure
Interactive Environments for SensiTive data Analytics

Effective and secure data processing within the European Open Science Cloud


Discover our solutions

Interactive dashboard

Portal with a set of tools and services to ease the secure sharing of sensitive data

security hardening

Secure computing platform providing a trusted execution environment

STORAGE

S3 storage management system for sharing the storage between teams

THEMATIC CATALOGS

Dedicated services catalogs, such as epidemiology, text anonymization or epidemiology

PRIVACY TOOLS

Specific anonymization and differential privacy tools can be used interactively



Use cases


Latest from EOSC-SIESTA:

  • EOSC-SIESTA at the 2026 EOSC Coordination Meeting in Brussels

    EOSC-SIESTA at the 2026 EOSC Coordination Meeting in Brussels

    The EOSC-SIESTA project took part in the fifth annual EOSC Coordination Meeting, held in Brussels on 8–9 July and hosted by the European Commission, alongside 33 other Horizon Europe-funded projects. During the meeting, Álvaro López García and Judith Sáinz-Pardo Díaz presented, on behalf of SIESTA, a success story on scaling cross-project collaboration between AI4EOSC, SIESTA… Read more..

  • SIESTA Concepts #5: Named Entity Recognition

    SIESTA Concepts #5: Named Entity Recognition

    Sharing cyber incident reports aids threat detection, but they contain personal data that must be anonymised. Since the Named Entity Recognition models behind anonymisation are usually trained in English, EOSC SIESTA researchers tested them on Spanish and found that multilingual models trained on target-language reports work best. Read more..

  • SIESTA tool: DatLeak

    SIESTA tool: DatLeak

    DatLeak is a tool developed by the Neurobiology Research Unit (NRU) at Copenhagen University Hospital and available through the SIESTA Dashboard. It detects data leakage in anonymized datasets: when a dataset is randomized or scrambled, the resulting variables can still carry traces of the original values that could link a record back to a real… Read more..


Consortium