EU research project

TITAN

Trusted AI systems through confidential computing and privacy-preserving technologies.

Duration
1 Feb 2024 — 31 Jan 2027
Total budget
€5 million
Status
Active
Funding programme
Horizon Europe
Project description

What TITAN sets out to do.

TITAN is a 36-month project that develops secure and trustworthy confidential data processing and sharing capabilities, and demonstrates them in the EOSC ecosystem.

The sharing of sensitive data follows FAIR data and open-science principles, with significant emphasis on privacy preservation and AI solutions aligned to EU ethical, regulatory, and legal boundaries. The open-source software platform focuses on two use cases: government data and healthcare.

Key objectives

  • Trusted AI infrastructure: Secure infrastructure for AI/ML workloads using confidential computing technologies.
  • Privacy-preserving ML: Collaborative machine learning that preserves data privacy across multiple stakeholders.
  • Secure data sharing: Secure multi-party computation protocols for confidential data exchange and processing.
  • TEE integration: Hardware-based security features that protect AI models and training data.

Expected outcomes

  • Confidential AI platform: A secure platform for training and deploying AI models in untrusted environments.
  • Privacy-preserving protocols: Advanced protocols for secure collaborative AI across organizations.
  • TEE-enabled ML: Machine-learning frameworks optimized for Trusted Execution Environments.
  • Secure model deployment: Tools and frameworks for deploying AI models with confidentiality guarantees.
Our involvement

Ultraviolet's role.

Ultraviolet brings extensive expertise in confidential computing and privacy-preserving technologies to TITAN. Our experience with TEEs, secure multi-party computation, and collaborative AI platforms positions us as a key contributor to developing trusted AI systems.

Confidential computing architecture
Designing and implementing TEE-based architectures for secure AI workloads.
Privacy-preserving AI frameworks
Frameworks for secure collaborative ML using SMPC and confidential computing.
Platform integration
Integrating confidential computing into existing AI/ML platforms and workflows.
Project partners

Collaborating with leading organizations across Europe.

— Get started

Trusted AI for regulated industries.

TITAN's research directly informs our healthcare and government AI deployments. Talk to the team.

€5M · Horizon Europe · Apache 2.0