Core Concepts
Introduction to Vectax Concepts
Table of Contents
- Fully Homomorphic Encryption (FHE)
- Vector Encryption
- RBAC (Role-Based Access Control)
- Format Preserving Encryption (FPE)
- Encrypted Inference
- Combined Features
Fully Homomorphic Encryption (FHE)
Overview
Fully Homomorphic Encryption (FHE) enables computation on encrypted data without requiring decryption, providing end-to-end security for sensitive data processing.
Use Cases
1. Privacy-Preserving Analytics
- Process sensitive data while encrypted
- Aggregate results securely
- Maintain data privacy
2. Secure Financial Computations
- Protected transaction processing
- Encrypted balance calculations
- Secure audit trails
3. Healthcare Analysis
- Process patient data securely
- Protected health metrics
- Compliant data analysis
Vector Encryption
Overview
Vector encryption enables secure storage and processing of vector embeddings while preserving their similarity properties.
Use Cases
1. Secure ML Model Feature Storage
- Store model embeddings securely
- Protect proprietary model features
- Enable secure model inference
2. Privacy-Preserving Analytics
- Analyze customer behavior patterns
- Secure storage of user preferences
- Protected A/B test results
RBAC (Role-Based Access Control)
Overview
RBAC provides fine-grained access control over encrypted data based on organizational roles and structure.
Use Cases
1. Enterprise Document Management
- Different access levels for departments
- Team-based document access
- Project-specific content restrictions
2. Healthcare Data Access
- Doctor/Patient data segregation
- Department-specific record access
- Regulatory compliance enforcement
Format Preserving Encryption (FPE)
Overview
FPE enables encryption while maintaining the original data format, crucial for systems with strict format requirements.
Use Cases
1. Customer Data Protection
- Credit card number encryption
- Social security number protection
- Phone number anonymization
2. Healthcare Records
- Patient ID encryption
- Medical record number protection
- Insurance ID encryption
Encrypted Inference
Overview
Encrypted inference combines FHE, vector encryption, and RBAC to enable secure ML model deployment and prediction.
Use Cases
1. Secure Model Deployment
- Protected model weights
- Secure inference pipeline
- Access-controlled predictions
2. Privacy-Preserving Predictions
- Secure input processing
- Protected feature extraction
- Confidential results
Combined Features
Overview
Mirror SDK enables powerful combinations of its security features for comprehensive solutions.
Use Cases
1. Secure Enterprise Search Platform
Combine RBAC, FPE, and Vector Search for:
- Department-specific document access
- Encrypted metadata handling
- Secure similarity search
- Audit trail maintenance
2. Healthcare Information System
Combine all features for:
- Patient record protection
- Doctor-specific access
- Secure case similarity search
- Protected medical prompts
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