Privacy Inference Module

The Bluwhale Protocol consolidates and authenticates identities from both Web2 and Web3 environments using Non-fungible tokens representing them as nodes in a vector graph structure. This process is facilitated through Bluwhale’s Knowledge Graph (User Oracle), external verification services, or direct on-chain attestations. Despite potential data falsification, the trustworthiness of data ultimately depends on the consumer's confidence in the endorsing party as well as the magnitude of references derived.

Data Attestation & Knowledge Graph

• User Graph: A decentralized Oracle service built on ChainLink infrastructure that imports user data from Web2 and Web3 sources, creating unique signatures to certify data authenticity.

• Data Providers: Entities like social media platforms can authenticate their data, providing strong endorsements through their signatures and individuals can opt-in to release and monetize their social profiles through Bluwhale.

• Third-party Verification: When direct authentication is not possible, third-party entities can endorse data, provided the verification process is robust and reference-based. Similar to the Google reference model, significance is defined by the number of references associated.

• On-chain Attestation: Utilizing Zero-Knowledge proofs or other attestation services, on-chain attestations in order to add another layer of validation and security.

Privacy and Computation

• Privacy Inference Module: Utilizes Trusted Execution Environments (TEEs) like Intel SGX for secure data processing. This module ensures that user data is being processed with privacy-enhancing technologies and enhanced via remote proof, identity authentication, session key exchange, and data encryption.

• TEE Node Processing: TEE nodes process data, producing attestations verified by the verification layer, ensuring the execution result posted on-chain is from a trusted source.

Computation & Training

• TEE Cluster: A cluster of TEE nodes performs specific functions. Nodes must stake tokens to participate, earning rewards for contributions and facing penalties for misconduct. Each task in the data processing workflow is handled by a single TEE node, which posts results on the blockchain with TEE attestation, timestamp, and nonce.

• AI Model Training: The protocol supports AI training within a TEE, enabling access to high-quality data in a privacy-preserving manner, protecting both user data and model parameters.

Security and ZK-Proofs

• TEEs and Security: TEEs provide attestations, verified through Intel SGX. The system is regularly updated to counter vulnerabilities, employing Oblivious RAM strategies and ensuring hardware integrity through Direct Anonymous Attestation protocols.

• Zero-Knowledge Proofs (zk-SNARKs): Used for validating and verifying data in a privacy-preserving manner, ensuring minimal data disclosure. Both ZK and non-ZK proof data are supported for verification and processing.

Execution Layer

• Data Transactions and Rewards: All data transactions are recorded and verifiable on the blockchain, enabling decentralized reward sharing. The execution layer operates within a multi-chain framework, orchestrating value distribution across the protocol's components through smart contracts on various consensus layers, including Layer 1 and Layer 2 networks.

• Primary Functions: Recording TEE attestations and verifier reports on-chain, allocating rewards to data providers and infrastructure, and levying charges on data consumers, ensuring a secure and efficient transaction process within the Bluwhale ecosystem.

Conclusion

The Bluwhale Protocol offers a comprehensive solution for identity consolidation and data authentication across Web2 and Web3 environments. By leveraging advanced technologies such as decentralized oracles, trusted execution environments, and zero-knowledge proofs, the protocol ensures data integrity, privacy, and security. Its robust framework for data transactions and reward distribution within a multi-chain environment underscores its commitment to a decentralized and efficient ecosystem.