LibFHE: A Numba-Based CUDA-Python Library for Non-RNS CKKS-BGV Fully Homomorphic Encryption on GPUs

• URL: http://arxiv.org/abs/2607.05920v3
• Date: Sat, 11 Jul 2026 04:48:56 GMT
• Title: LibFHE: A Numba-Based CUDA-Python Library for Non-RNS CKKS-BGV Fully Homomorphic Encryption on GPUs
• Abstract summary: It has been a decade since the fourth-generation FHE framework, CKKS, was proposed; yet, there is still no indicator pointing toward a fifth-generation successor.<n>We propose LibFHE, a high-performance GPU-accelerated framework that features GPU-Python bindings to achieve both high-level programmability and bare-metal performance for homomorphic workloads.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It has been a decade since the fourth-generation FHE framework, CKKS, was proposed; yet, there is still no indicator pointing toward a fifth-generation successor; and in recent years, numerous studies have explored GPU acceleration to improve the efficiency of homomorphic computations. In this paper, we propose LibFHE, a high-performance GPU-accelerated framework that features CUDA-Python bindings to achieve both high-level programmability and bare-metal GPU performance for homomorphic workloads. A large majority of state-of-the-art implementations adopt the RNS-CKKS variant. In contrast, this work deliberately revisits the original (non-RNS) CKKS-BGV framework, and develops a GPU-based implementation along with corresponding optimizations. Experimental results demonstrate that optimized CUDA-Python implementations can achieve performance comparable to highly optimized CPU-based C++ FHE libraries, while significantly reducing implementation complexity and improving programmability.

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