Encryption

Fully Homomorphic Encryption Playground Supporting FHE, Search Engine & Benchmarks

Features

Core FHE Operations

• 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

Encrypted Search Engine

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

Performance Benchmarking

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

Quick Start

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

Architecture

FHE Context (pkg/fhe/context.go)

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

Encrypted Search (pkg/search/encrypted_search.go)

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

Security Parameters

Parameter Set	Poly Degree	Security Level	Performance
Small	1024	~80 bits	Fast
Medium	2048	~128 bits	Moderate
Large	4096	~256 bits	Slow

Applications Demonstrated

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

Extending the Playground

Adding New Schemes

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

Custom Applications

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

Performance Notes

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

Integration with Production Libraries

For production use, consider integrating with:

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

Educational Resources

This playground demonstrates:

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