Organizations across industries struggle to collaborate on sensitive data due to privacy regulations, security concerns, and the risk of exposing confidential information. Traditional methods of data sharing often require complex legal agreements and technical safeguards, hindering innovation and limiting the potential for valuable insights.

Decentriq provides a data clean room platform that enables secure data collaboration without exposing the underlying data. The platform utilizes privacy-enhancing technologies like confidential computing to ensure that sensitive information remains encrypted and inaccessible during processing. This allows organizations to analyze and derive insights from combined datasets while maintaining data ownership and complying with privacy regulations. Decentriq's platform supports various use cases, including marketing, healthcare research, and advanced analytics, enabling organizations to unlock the full value of their data through secure and compliant collaboration. The platform offers both a no-code interface for business users and custom collaboration options with support for Python, SQL, and R for data scientists.

Secure data clean rooms that enable collaboration without data sharing

Privacy-enhancing technologies, including confidential computing, to protect sensitive data

Support for various data collaboration use cases across industries

No-code interface for business users to unlock insights without programming

Custom collaboration options with support for Python, SQL, and R for advanced analytics

Robust access control and seamless integration with Single Sign-On (SSO)

Enterprise-grade security and scalability to meet stringent requirements

Compliance with GDPR and other data privacy regulations

Direct data upload and connectors for easy and secure data onboarding

End-to-end data encryption

Decentriq's primary customers include organizations in privacy-conscious industries such as advertising, healthcare, finance, and government, as well as data-driven companies seeking to collaborate on sensitive data without compromising confidentiality.