Confidential Consortium Framework




The Confidential Consortium Framework (CCF), a joint project with Azure Blockchain, is an open-source framework for building a new category of secure, highly available, and performant applications that focus on multi-party compute and data. While not limited just to blockchain applications, CCF can enable high-scale, confidential blockchain networks that meet key enterprise requirements — providing a means to accelerate production enterprise adoption of blockchain technology.

Leveraging the power of trusted execution environments (TEEs), decentralized systems concepts, and cryptography, CCF enables enterprise-ready computation or blockchain networks that deliver:

  • Throughput and latency approaching database speeds. Through its use of TEEs, the framework creates a network of remotely attestable enclaves. This gives a web of trust across the distributed system, allowing a user that verifies a single cryptographic quote from a CCF node to effectively verify the entire network. This simplifies consensus and thus improves transaction speed and latency — all without compromising security or assuming trust.
  • Richer, more flexible confidentiality models. Beyond safeguarding data access with encryption-in-use via TEEs, we use industry standards (TLS and remote attestation) to ensure secure node communication. Transactions can be processed in the clear or revealed only to authorized parties, without requiring complicated confidentiality schemes.
  • Network and service policy management through non-centralized governance. The framework provides a network and service configuration to express and manage consortium and multi-party policies. Governance actions, such as adding members to the governing consortium or initiating catastrophic recovery, can be managed and recorded through standard ledger transactions agreed upon via stakeholder voting.
  • Improved efficiency versus traditional blockchain networks. The framework improves on bottlenecks and energy consumption by eliminating computationally intensive consensus algorithms for data integrity, such as proof-of-work or proof-of-stake.


Current Team


  • Portrait of Christine Avanessians

    Christine Avanessians

    Principal PM

  • Portrait of Sid Krishna

    Sid Krishna

    Principal Software Engineer


  • Portrait of Thomas Moscibroda

    Thomas Moscibroda

    Partner Research Scientist Azure Compute