YUENIX-FHE
CUDA-Accelerated Fully Homomorphic Encryption
Compute on encrypted data at GPU speed. YUENIX-FHE supports BGV, BFV, CKKS, and TFHE/FHEW schemes — including CKKS-to-TFHE/FHEW scheme switching — with a clean three-layer architecture and CUDA-native optimizations built for NVIDIA GPUs.
Three-Layer Architecture
Scheme Layer
High-level FHE operationsRNS Arithmetic Layer
Residue Number System computationsCore Arithmetic Layer
Foundational arithmetic operationsPowered by NVIDIA CUDA
Supported Schemes
Exact integer arithmetic with noise management — ideal for counting, voting, and database queries on encrypted data.
Modular integer arithmetic without rescaling — suited for simple encrypted computations with predictable noise budgets.
Approximate arithmetic on encrypted real numbers — designed for machine learning inference and statistical analysis.
Bit-level Boolean circuit evaluation with fast bootstrapping — enables arbitrary function evaluation on encrypted bits.
GPU Optimizations
Multi-GPU Scaling
Distribute FHE workloads across multiple GPUs with automatic load balancing, scaling throughput linearly as you add devices.
Dynamic Resource Allocation
Scalable resource management that adapts GPU memory and compute allocation in real time based on workload demands.
Kernel Fusion
Intra-kernel and inter-kernel fusion reduces launch overhead and overlaps computation with memory transfers for maximum throughput.
Enhanced Hybrid Key-Switching
Optimized key-switching procedure that reduces memory bandwidth and computation by leveraging GPU shared memory.
GPU Memory Pool
Custom allocator that pre-allocates and recycles GPU memory blocks, eliminating allocation latency during FHE operations.
RNS & NTT Optimization
Residue Number System arithmetic and asynchronous Number Theoretic Transforms tuned for GPU warp-level primitives.
Applications & Use Cases
Cryptographic Primitives
Privacy-Preserving ML
Train and run inference on encrypted datasets — models never see raw data, enabling collaborative ML without data exposure.
Private Information Retrieval
Query databases without revealing what you searched for. The server processes the query on encrypted indices and returns encrypted results.
Private Set Intersection
Two parties discover shared records without revealing anything else — critical for fraud detection, contact tracing, and ad measurement.
Industry Scenarios
Healthcare
Analyse patient records, genomic data, and clinical trials across institutions without exposing sensitive health information.
Finance
Run risk models, compliance checks, and cross-institutional analytics on encrypted financial data — meeting regulatory requirements by design.
Government
Enable secure inter-agency data sharing, encrypted census processing, and privacy-preserving national statistics without centralised data access.
System Requirements
Start Computing Encrypted
Interested in YUENIX-FHE for your project? Get in touch to learn more about integration and licensing.