Field-Level Encryption for Vectors

Overview

NeuronDB provides field-level encryption for sensitive vector data and metadata using AES-256-GCM encryption. This ensures data confidentiality at rest while maintaining query capabilities.

Features

• AES-256-GCM encryption for vectors at rest
• Transparent encryption/decryption using SQL functions
• Support for per-column encryption keys
• Key rotation capabilities

Prerequisites

Field-level encryption requires OpenSSL support in the PostgreSQL build. The encryption functions use OpenSSL's EVP API for AES-256-GCM.

Configuration

Enable encryption:

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SET neurondb.encryption_enabled = true;

Usage

Encrypting Vectors

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-- Encrypt a vector column
CREATE TABLE sensitive_documents (
    id SERIAL PRIMARY KEY,
    content TEXT,
    embedding vector(384),
    encrypted_embedding bytea
);

-- Insert with encryption
INSERT INTO sensitive_documents (content, encrypted_embedding)
VALUES (
    'Sensitive content',
    encrypt_vector(
        '[0.1, 0.2, 0.3, ...]'::vector(384),
        'my-encryption-key-12345'
    )
);

Decrypting Vectors

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-- Decrypt for use in queries
SELECT 
    id,
    content,
    decrypt_vector(encrypted_embedding, 'my-encryption-key-12345') AS embedding
FROM sensitive_documents;

Key Rotation

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-- Rotate encryption key for a column
SELECT rotate_encryption_key(
    'sensitive_documents',
    'encrypted_embedding',
    'old-encryption-key',
    'new-encryption-key'
);

Storage Format

Encrypted vectors are stored as BYTEA containing the EncryptedVector structure:

• Encryption IV (12 bytes)
• Authentication tag (16 bytes)
• Dimension information
• Encrypted ciphertext

Security Considerations

1. Key Management: Store encryption keys securely. Consider using PostgreSQL's key management extensions or external key management systems.

2. Performance: Encryption/decryption adds latency. Use encryption selectively for sensitive data only.

3. Key Rotation: Regularly rotate encryption keys for enhanced security.

4. Backup: Encrypted data in backups remains encrypted. Ensure backup keys are also securely managed.

Limitations

• Encrypted vectors cannot be used directly in vector similarity searches. Decrypt before querying.
• Key management is the responsibility of the database administrator.
• Encryption functions require OpenSSL support.

Related Topics

• Security Overview
• RLS for Embeddings
• Audit Logging