Homomorphic Encryption
Revolutionary cryptographic technology that enables computation on encrypted data without decryption, ensuring 100% privacy for AI memory operations.
What is Homomorphic Encryption?
Homomorphic encryption is a groundbreaking cryptographic technique that allows mathematical operations to be performed on encrypted data without revealing the underlying information. This means AI agents can process and share memories while keeping all data completely private.
Privacy Benefits
Zero-Knowledge Processing
Perform computations without revealing input data
End-to-End Privacy
Data never exposed, even during processing
Compliance Ready
Meets GDPR, HIPAA, and other privacy standards
Agent Collaboration
Secure multi-agent workflows without trust issues
How Homomorphic Encryption Works
The process involves three key phases: encryption with special mathematical properties, computation on the encrypted data, and decryption to reveal results without exposing intermediate values.
Data is encrypted using lattice-based cryptography with special homomorphic properties.
plaintext → ciphertextMathematical operations are performed directly on encrypted data without decryption.
Enc(a) ⊕ Enc(b) = Enc(a+b)Results are decrypted to reveal the computed output without exposing intermediate values.
ciphertext → resultTechnical Implementation
infaza implements a hybrid approach combining multiple homomorphic encryption schemes optimized for different types of AI operations.
CKKS Scheme
Optimized for floating-point operations and machine learning computations. Ideal for vector embeddings and neural network operations.
Use Cases
- • Vector similarity calculations
- • Embedding transformations
- • Statistical analysis
- • Approximate computations
Performance
BGV Scheme
Designed for exact integer arithmetic and boolean operations. Perfect for discrete data processing and logical operations.
Use Cases
- • Exact counting operations
- • Boolean logic circuits
- • Discrete feature analysis
- • Integer-based computations
Performance
Supported Operations
Our homomorphic encryption implementation supports a comprehensive set of operations enabling complex AI computations on encrypted data.
Enc(a) + Enc(b)Enc(a) × Enc(b)k × Enc(a)P(Enc(x))⟨Enc(v₁), Enc(v₂)⟩Enc(A) × Enc(B)σ(Enc(x))Σ Enc(xᵢ)Performance Characteristics
While homomorphic encryption introduces computational overhead, our optimized implementation achieves practical performance for real-world AI applications.
Addition
Multiplication
Encryption
Optimization Techniques
Hardware Acceleration
- • GPU-accelerated number theory transforms
- • SIMD vectorization for parallel operations
- • Custom silicon for lattice operations
Algorithmic Optimizations
- • Batching for SIMD-style operations
- • Lazy relinearization and rescaling
- • Bootstrapping optimization
Real-World Use Cases
Multiple AI agents can collaborate on sensitive tasks without revealing their individual knowledge or training data.
Run AI model inference on encrypted inputs, ensuring neither the model nor the input data is exposed.
Enable federated learning where model updates are encrypted, preserving privacy across distributed training.
Perform joint analytics on sensitive datasets from multiple parties without revealing individual contributions.
Security Guarantees
Our homomorphic encryption implementation provides mathematically proven security guarantees based on well-established cryptographic assumptions.
Security Properties
Semantic Security
Ciphertexts reveal no information about plaintexts
IND-CPA Security
Indistinguishable under chosen-plaintext attacks
Post-Quantum Security
Resistant to quantum computer attacks
Circuit Privacy
Computation patterns remain hidden
Multi-Party Security
Secure against colluding parties
Correctness
Guaranteed accurate computation results