Decentriq is a pioneer in confidential computing and secure data collaboration, enabling organizations to extract insights from sensitive data without compromising privacy or control. Built on trusted execution environments (TEEs) and their cryptographic guarantees, Decentriq’s platform allows multiple parties to perform computations on combined datasets while ensuring that raw data remains encrypted and inaccessible, even to the platform operator.

With a strong track record across healthcare, life sciences, and other regulated industries, Decentriq empowers institutions to collaborate on high-value use cases that are otherwise impossible due to privacy, regulatory, or competitive constraints.

Decentriq’s role in SEARCH

The vision of SEARCH is to unlock the full potential of biomedical data for personalised medicine by overcoming institutional silos and enabling secure, large-scale AI analysis. Instead of centralised data sharing, SEARCH combines synthetic data generation and federated learning, ensuring that sensitive information remains protected while still enabling advanced research and clinical applications.

Within this framework, Decentriq provides secure computation capabilities that complement federated learning. While federated learning excels at distributed model training, many biomedical research workflows require more flexible and exploratory computations—such as cohort discovery, multi-party record linkage, or cross-institutional quality assessments. For these cases, Decentriq’s platform acts as a trusted data clean room, allowing authorized researchers to perform sophisticated computations on pooled sensitive data within a secure enclave.

Trust, compliance, and healthcare impact

Decentriq’s technology ensures that patient-level data from multiple institutions can be analyzed collectively without any party, including Decentriq itself, accessing the raw information. Through hardware-backed TEEs, all data remains encrypted in memory and at rest, with cryptographic attestation verifying that only approved code runs on approved data. This approach meets strict regulatory requirements such as GDPR, HIPAA, and emerging AI regulations, while enabling use cases beyond traditional federated learning—like iterative analyses or real-time interactive queries.

By integrating with the SEARCH infrastructure, Decentriq provides a secure computing layer where data providers maintain sovereignty while enabling collaborative analysis. This is crucial for clinical validation studies, regulatory submissions, and cross-border collaborations where trust and compliance are essential.

Beyond the technical contribution, Decentriq supports the long-term sustainability of SEARCH by demonstrating how confidential computing bridges research innovation and real-world clinical deployment. It ensures sensitive health data can be used responsibly and securely for AI model development, validation, and deployment—advancing personalised medicine while maintaining the trust of patients, clinicians, and regulators.

Through this role, Decentriq ensures that SEARCH can support the full spectrum of secure biomedical computation: from federated model training at the edge to interactive, privacy-preserving analysis in trusted enclaves—creating a comprehensive, future-proof ecosystem for responsible health data collaboration across Europe and beyond.