Enables secure, privacy-preserving data collaboration through confidential computing technology that provides cryptographic guarantees of data protection, allowing organizations to unlock insights from sensitive data without compromising privacy or regulatory compliance.
Last updated Feb 18, 2026 by AI Enrichment
Emerging player in the data clean room space with focus on confidential computing technology
Decentriq is a data clean room and secure data collaboration platform that leverages confidential computing technology to enable organizations to share and analyze sensitive data without exposing the underlying information. The company's platform is built on trusted execution environments (TEEs) and provides cryptographic guarantees of data privacy, making it particularly relevant for advertising, media, and marketing use cases where multiple parties need to collaborate on sensitive customer data while maintaining compliance with privacy regulations. In the AdTech ecosystem, Decentriq serves as an alternative to traditional data clean rooms offered by walled gardens, providing a neutral infrastructure where advertisers, publishers, and data providers can collaborate on audience insights, measurement, and attribution without compromising data security. The platform supports use cases including cross-publisher collaboration, brand safety verification, and privacy-compliant audience targeting. Decentriq differentiates itself through its use of confidential computing hardware enclaves, which provide stronger technical guarantees of data privacy compared to policy-based clean rooms. This approach appeals to enterprises in highly regulated industries and organizations seeking maximum control over their data sharing arrangements.
Privacy-preserving environment for secure data collaboration and analytics across multiple parties
Infrastructure built on trusted execution environments (TEEs) for cryptographically secure data processing
Tools for advertisers and publishers to collaborate on audience insights, measurement, and attribution