CHEHAB RL: 学习优化完全同态加密计算
CHEHAB RL: Learning to Optimize Fully Homomorphic Encryption Computations
- New York University Abu Dhabi（纽约大学阿布扎比分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
CHEHAB RL通过深度强化学习自动优化FHE代码，提升执行速度和减少噪声，编译过程更高效。
AI中文摘要：
完全同态加密（FHE）允许直接在加密数据上进行计算，但其高昂的计算成本仍然是一个显著的障碍。编写高效的FHE代码是一个需要密码学专业知识的复杂任务，而找到最优的程序转换序列通常是不可行的。在本文中，我们提出了一种名为CHEHAB RL的新框架，利用深度强化学习（RL）来自动化FHE代码优化。与依赖预定义启发式或组合搜索不同，我们的方法训练一个RL代理，学习应用一系列重写规则的高效策略，以自动向量化标量FHE代码，同时减少指令延迟和噪声增长。所提出的方法支持结构化和非结构化代码的优化。为了训练代理，我们利用大型语言模型（LLM）合成了一组多样化的计算数据集。我们将所提出的方法整合到CHEHAB FHE编译器中，并在一系列基准测试中评估其性能，与最先进的向量化FHE编译器Coyote进行比较。结果表明，我们的方法生成的代码在执行速度上快5.3倍，噪声累积减少2.54倍，而编译过程本身比Coyote快27.9倍（几何平均数）。
英文摘要：
Fully Homomorphic Encryption (FHE) enables computations directly on encrypted data, but its high computational cost remains a significant barrier. Writing efficient FHE code is a complex task requiring cryptographic expertise, and finding the optimal sequence of program transformations is often intractable. In this paper, we propose CHEHAB RL, a novel framework that leverages deep reinforcement learning (RL) to automate FHE code optimization. Instead of relying on predefined heuristics or combinatorial search, our method trains an RL agent to learn an effective policy for applying a sequence of rewriting rules to automatically vectorize scalar FHE code while reducing instruction latency and noise growth. The proposed approach supports the optimization of both structured and unstructured code. To train the agent, we synthesize a diverse dataset of computations using a large language model (LLM). We integrate our proposed approach into the CHEHAB FHE compiler and evaluate it on a suite of benchmarks, comparing its performance against Coyote, a state-of-the-art vectorizing FHE compiler. The results show that our approach generates code that is $5.3\times$ faster in execution, accumulates $2.54\times$ less noise, while the compilation process itself is $27.9\times$ faster than Coyote (geometric means).
