Fully Homomorphic Encryption Playground Supporting FHE, Search Engine & Benchmarks

• BFV Scheme Implementation: Brakerski-Fan-Vercauteren homomorphic encryption
• Key Generation: Secure public/private key pair generation
• Homomorphic Operations: Addition and multiplication on encrypted data
• Noise Management: Gaussian noise for security

• Private Search: Search encrypted documents without revealing queries
• Term Indexing: Encrypted inverted index for efficient searches
• Score Ranking: Relevance scoring on encrypted data

• Multiple Parameter Sets: Compare different security levels
• Operation Timing: Measure encryption, decryption, and computation times
• Memory Usage: Monitor resource consumption

git clone https://github.com/WillKirkmanM/encryption
cd encryption
go run main.go

• Polynomial ring operations
• Modular arithmetic
• Noise budget management
• Key generation and management

• Document indexing with encryption
• Private information retrieval
• Homomorphic query processing

1. Private Database Queries: Search without revealing search terms
2. Secure Computation: Arithmetic on encrypted financial data
3. Privacy-Preserving Analytics: Statistics on encrypted datasets

// Implement different FHE schemes
type BGVContext struct { ... }
type CKKSContext struct { ... } // For approximate arithmetic

// Add your own encrypted computations
func (ctx *BFVContext) CustomOperation(ct1, ct2 *Ciphertext) (*Ciphertext, error) {
    // Your homomorphic operation here
}

• Key Generation: Most expensive operation
• Multiplication: Increases noise, requires key switching
• Circuit Depth: Limited by noise budget
• Parallelisation: Many operations can be vectorized

For production use, consider integrating with:

• Microsoft SEAL (C++ with Go bindings)
• HElib (C++ library)
• PALISADE (C++ framework)

This playground demonstrates:

• Ring Learning With Errors (RLWE) problem
• Polynomial modular arithmetic
• Noise growth in homomorphic operations
• Trade-offs between security and performance