PyTFHE: An End-to-End Compilation and Execution Framework for Fully Homomorphic Encryption Applications
PyTFHE: An End-to-End Compilation and Execution Framework for Fully Homomorphic Encryption Applications
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DOI：
10.1109/ispass57527.2023.00012
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发表时间：
2023-04
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通讯作者：
Jiaao Ma;Ceyu Xu;Lisa Wu Wills
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作者：
Jiaao Ma;Ceyu Xu;Lisa Wu Wills
完全同态加密 (FHE) 是一种功能强大的加密方案，可以对加密数据进行计算，从而允许客户端将计算卸载给不受信任的第三方，而不会损害数据隐私。然而，由于 FHE 巨大的计算开销，尚未得到广泛采用。此外，加密软件开发需要专门的专业知识，并且在将 FHE 应用于广泛的应用时提出了重大挑战。我们推出了 PyTFHE，这是一个框架，它通过实现高效的 FHE 应用程序开发和更高效的 FHE 应用程序执行来解决这些困难。 PyTFHE 构建在 TFHE（环面快速全同态加密）方案之上，这是一种支持门级评估和任意深度布尔电路的 FHE 方案。 PyTFHE 采用 TFHE 特定方法设计，允许对 TFHE 应用程序进行最先进的优化。具体来说，PyTFHE 具有 ChiselTorch，这是第一个允许使用与 PyTorch 兼容的 API 轻松生成保护隐私的深度神经网络模型的编译器。 PyTFHE 也是第一个采用强大后端的 FHE 框架，可在分布式 CPU 系统和高性能 GPU 上高效执行 TFHE 应用程序。我们通过使用 VIP-Bench 对框架进行基准测试、实施隐私保护深度神经网络并评估其在各种系统上的性能来展示 PyTFHE 的有效性。我们将生成的 TFHE 程序执行性能与三个现有框架（Google Transpiler、Cingulata 和 E3）进行了比较。我们证明 PyTFHE 实现了一到两个数量级的性能优势。
Fully Homomorphic Encryption (FHE) is a powerful cryptographic scheme that enables computation on encrypted data, which allows clients to offload computation to an untrusted third party without compromising data privacy. However, FHE has not yet been widely adopted due to its enormous computational overhead. Further, cryptographic software development requires specialized expertise and presents a significant challenge when applying FHE to a broad range of applications. We present PyTFHE, a framework that tackles these difficulties by enabling highly productive FHE application development and orders of magnitude more efficient FHE application execution. PyTFHE is built on top of the TFHE (Fast Fully Homomorphic Encryption over the Torus) scheme, which is an FHE scheme that supports gate-level evaluation and arbitrary depth of boolean circuits. PyTFHE is designed in a TFHE-specific approach, allowing state-of-the-art optimizations for TFHE applications. Specifically, PyTFHE features ChiselTorch, the first compiler that allows easy generations of privacy-preserving deep neural network models with PyTorch-compatible APIs. PyTFHE is also the first FHE framework that employs a powerful backend enabling efficient execution of TFHE applications on distributed CPU systems and high-performance GPUs. We demonstrate the effectiveness of PyTFHE by benchmarking the framework using VIP-Bench and implementing privacy-preserving deep neural networks and evaluating their performance on various systems. We compare the performance of our generated TFHE program execution with three existing frameworks, Google Transpiler, Cingulata, and E3. We show that PyTFHE achieves one to two orders of magnitude performance advantage.