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FHE

One of the core technologies planned to be leveraged by PrivateAI to ensure data security and privacy is Fully Homomorphic Encryption (FHE). This advanced encryption method allows computations to be conducted directly on encrypted data without needing to decrypt it first. This ensures that the AI models will interact only with FHE-encrypted data, safeguarding the original user-uploaded information throughout the processing pipeline.

FHE is important in enhancing data privacy and security within PrivateAI by offering several key benefits:

  • Confidentiality: FHE enables secure data processing without the need for decryption. This means that even while data is being used for computation, it remains encrypted, ensuring that unauthorized parties cannot access the underlying information.
  • Privacy-preserving analytics: By allowing computations on encrypted data, FHE facilitates analytics and machine learning tasks without exposing raw data. This is crucial for maintaining user privacy, as it ensures that sensitive information is never revealed, even during complex data operations.
  • Secure data sharing: FHE supports secure data sharing by enabling operations on encrypted data across different entities without disclosing the actual values. This capability is particularly important for collaborative environments where data needs to be processed or analyzed by multiple stakeholders while keeping the original data confidential.

Principles of Fully Homomorphic Encryption in PrivateAI​

To uphold the integrity and effectiveness of the FHE system, PrivateAI is going to adhere to three guiding principles:

  • Data privacy: Private data must remain private, accessible only to authorized users.
  • Preservation of information: Operations performed on encrypted data must preserve the integrity of the information without alteration or loss.
  • Protocol compliance: Third-party servers that perform computations on encrypted data must strictly adhere to pre-defined protocol rules.

Planned Utilization of FHE in PrivateAI​

The PrivateAI platform intends to utilize FHE through various mechanisms to ensure data security and privacy:

  • When users upload data, it will be encrypted using FHE before any processing begins, keeping the original data hidden during computations.
  • The AI models within PrivateAI will be equipped to work with FHE-encrypted data, allowing both model training and inference to be conducted on these encrypted datasets, thereby maintaining confidentiality throughout the AI lifecycle.
  • FHE will support federated computation across the PrivateAI infrastructure's nodes. These nodes can execute operations on encrypted data without the need for decryption, ensuring secure and efficient data processing across the network.
  • PrivateAI will enable users to share encrypted data for collaborative analytics and machine learning. With FHE, this collaboration will not compromise data privacy, as all computations will occur on encrypted data.

By implementing these technologies and principles, PrivateAI aims to ensure that sensitive data remains protected throughout all stages of use, from input to processing and analysis.