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CryptOracle: A Modular Framework to Characterize Fully Homomorphic Encryption

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arXiv:2510.03565v4 Announce Type: replace Abstract: 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

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Computer Science > Cryptography and Security [Submitted on 3 Oct 2025 (v1), last revised 27 Mar 2026 (this version, v4)] CryptOracle: A Modular Framework to Characterize Fully Homomorphic Encryption Cory Brynds, Parker McLeod, Lauren Caccamise, Asmita Pal, Dewan Saiham, Sazadur Rahman, Joshua San Miguel, Di Wu 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 this https URL. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2510.03565 [cs.CR] (or arXiv:2510.03565v4 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2510.03565 Focus to learn more Submission history From: Cory Brynds [view email] [v1] Fri, 3 Oct 2025 23:30:29 UTC (603 KB) [v2] Mon, 13 Oct 2025 01:04:18 UTC (603 KB) [v3] Mon, 15 Dec 2025 19:06:17 UTC (611 KB) [v4] Fri, 27 Mar 2026 15:20:23 UTC (610 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev | next > new | recent | 2025-10 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

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