Chameleon: An Efficient FHE Scheme Switching Acceleration on GPUs

Fully homomorphic encryption (FHE) enables direct computation on encrypted data, making it a crucial technology for privacy protection. However, FHE suffers from significant performance bottlenecks. In this context, GPU acceleration offers a promising solution to bridge the performance gap. Existing efforts primarily focus on single-class FHE schemes, which fail to meet the diverse requirements of data types and functions, prompting the development of hybrid multi-class FHE schemes. However, studies have yet to thoroughly investigate specific GPU optimizations for hybrid FHE schemes. In this paper, we present an efficient GPU-based FHE scheme switching acceleration named Chameleon. First, we propose a scalable NTT acceleration design that adapts to larger CKKS polynomials and smaller TFHE polynomials. Specifically, Chameleon tackles synchronization issues by fusing stages to reduce synchronization, employing polynomial coefficient shuffling to minimize synchronization scale, and utilizing an SM-aware combination strategy to identify the optimal switching point. Second, Chameleon is the first to comprehensively analyze and optimize critical switching operations. It introduces CMux-level parallelization to accelerate LUT evaluation and a homomorphic rotation-free matrixvector multiplication to improve repacking efficiency. Finally, Chameleon outperforms the state-of-the-art GPU implementations by 1.23× in CKKS HMUL and 1.15× in bootstrapping. It also achieves up to 4.87× and 1.51× speedups for TFHE bootstrapping compared to CPU and GPU versions, respectively, and delivers a 67.3× average speedup for scheme switching over CPU-based implementation.