Data Encryption & Privacy Measures

In OmniTensor, data security and privacy are paramount for business users deploying AI models on the decentralized infrastructure. The following mechanisms are implemented to ensure the highest levels of confidentiality, integrity, and compliance:

End-to-End Encryption

All data handled within the OmniTensor ecosystem is secured through end-to-end encryption (E2EE). This ensures that any data, whether in transit or at rest, is encrypted with advanced cryptographic standards (e.g., AES-256). This prevents unauthorized access or interception during data exchanges across decentralized nodes.

• Data in Transit

  When AI models, datasets, or inference requests are transmitted between nodes, they are encrypted using TLS 1.3, ensuring secure communication channels. This protects against man-in-the-middle (MITM) attacks.

• Data at Rest

  Sensitive data, including model parameters, training datasets, and business-specific outputs, are encrypted while stored across decentralized nodes. OmniTensor supports the use of secure enclaves and hardware-backed encryption for higher levels of security, particularly for sensitive AI computations.

Zero-Knowledge Proofs

To further enhance privacy, OmniTensor incorporates zero-knowledge proof (ZKP) protocols. These cryptographic techniques allow the verification of AI computations and model inference without revealing the underlying data. This ensures that sensitive data processed on the decentralized infrastructure remains confidential, even from the node operators.

Example:

zk-SNARK verification used during model inference allows node validators to confirm the correctness of the AI task without access to raw business data or model weights.

Homomorphic Encryption for AI Computation

For highly sensitive data, OmniTensor enables the use of homomorphic encryption. This allows AI computations to be performed on encrypted data without decrypting it, ensuring that the underlying information remains private even while being processed. This is particularly useful for businesses handling personal or proprietary data, such as medical records or financial transactions.

Example:

# Example of homomorphic encryption for AI computation
from pycrypto import PaillierEncryption

paillier = PaillierEncryption()
encrypted_data = paillier.encrypt(business_sensitive_data)

# Perform computation on encrypted data
encrypted_result = ai_model.compute(encrypted_data)

# Decrypt the result after computation
result = paillier.decrypt(encrypted_result)

Compliance with Data Privacy Regulations

OmniTensor ensures compliance with various data protection standards, including GDPR, CCPA, and HIPAA, by providing transparent control over data handling and access policies. Businesses can define their data retention policies and decide how long AI models and datasets remain on the network. All user interactions are auditable through immutable blockchain records, providing full traceability.

Access Control and Role-Based Permissions

To further enhance data security, OmniTensor integrates role-based access control (RBAC). This ensures that only authorized personnel within a business can access specific models or datasets. Multi-signature authentication mechanisms are employed to control sensitive actions, such as model deployments or data deletions.

# Example RBAC configuration
users:
  - id: "user1"
    roles:
      - "admin"
      - "model_deployer"
  - id: "user2"
    roles:
      - "data_analyst"