Privacy-preserving machine learning has become an important long-term pursuit in this era of artificial intelligence (AI). Fully Homomorphic Encryption (FHE) is a uniquely promising solution, offering provable privacy and security guarantees. Unfortunately, computational cost is impeding its mass adoption. Modern solutions are up to six orders of magnitude slower than plaintext execution. Understanding and reducing this overhead is essential to the advancement of FHE, particularly as the underlying algorithms evolve rapidly. This paper presents a detailed characterization of OpenFHE, a comprehensive open-source library for FHE, with a particular focus on the CKKS scheme due to its significant potential for AI and machine learning applications. We introduce CryptOracle, a modular evaluation framework comprising (1) a benchmark suite, (2) a hardware profiler, and (3) a predictive performance model. The benchmark suite encompasses OpenFHE kernels at three abstraction levels: workloads, microbenchmarks, and primitives. The profiler is compatible with standard and user-specified security parameters. CryptOracle monitors application performance, captures microarchitectural events, and logs power and energy usage for AMD and Intel systems. These metrics are consumed by a modeling engine to estimate runtime and energy efficiency across different configuration scenarios, with error geomean of $-7.02\%\sim8.40\%$ for runtime and $-9.74\%\sim15.67\%$ for energy. CryptOracle is open source, fully modular, and serves as a shared platform to facilitate the collaborative advancements of applications, algorithms, software, and hardware in FHE. The CryptOracle code can be accessed at https://github.com/UnaryLab/CryptOracle.

翻译：隐私保护机器学习已成为人工智能时代一项重要的长期追求。全同态加密（FHE）作为一种极具前景的解决方案，能够提供可证明的隐私与安全保障。然而，其计算成本阻碍了大规模应用。现代解决方案的执行速度比明文处理慢多达六个数量级。理解并降低这种开销对于FHE的发展至关重要，尤其是在底层算法快速演进的背景下。本文对OpenFHE——一个全面的全同态加密开源库——进行了详细表征，并重点分析了CKKS方案因其在人工智能与机器学习应用中的巨大潜力。我们提出了CryptOracle，这是一个模块化评估框架，包含（1）基准测试套件，（2）硬件性能分析器，以及（3）预测性能模型。该基准测试套件涵盖OpenFHE在三个抽象层次的内核：工作负载、微基准测试与基础原语。分析器兼容标准及用户自定义的安全参数。CryptOracle能够监测应用性能、捕获微架构事件，并记录AMD与Intel系统的功耗与能耗数据。这些指标由建模引擎用于估算不同配置场景下的运行时间与能效，其运行时间预测误差的几何平均值为$-7.02\%\sim8.40\%$，能耗预测误差为$-9.74\%\sim15.67\%$。CryptOracle采用开源、全模块化设计，可作为促进FHE在应用、算法、软件及硬件方面协同发展的共享平台。CryptOracle代码可通过https://github.com/UnaryLab/CryptOracle 访问。