Beyond Latency: A System-Level Characterization of MPC and FHE for PPML

Computer Science > Cryptography and Security [Submitted on 31 Mar 2026] Beyond Latency: A System-Level Characterization of MPC and FHE for PPML Pengzhi Huang, Kiwan Maeng, G. Edward Suh Privacy protection has become an increasing concern in modern machine learning applications. Privacy-preserving machine learning (PPML) has attracted growing research attention, with approaches such as secure multiparty computation (MPC) and fully homomorphic encryption (FHE) being actively explored. However, existing evaluations of these approaches have frequently been done on a narrow, fragmented setup and only focused on a specific performance metric, such as the online inference latency of a specific batch size. From the existing reports, it is hard to compare different approaches, especially when considering other metrics like energy/cost or broader system setups (various hyperparameters, offline overheads, future hardware/network configurations, etc.). We present a unified characterization of three popular approaches -- two variants of MPC based on arithmetic/binary sharing conversion and function secret sharing, and FHE -- on their performance and cost in performing privacy-preserving inference on multiple CNN and Transformer models. We study a range of LAN and WAN environments, model sizes, batch sizes, and input sequence lengths. We evaluate not only the performance but also the energy consumption and monetary cost of deploying under a realistic scenario, taking into account their offline and online computation/communication overheads. We provide empirical guidance for selecting, optimizing, and deploying these privacy-preserving compute paradigms, and outline how evolving hardware and network trends are likely to shift trade-offs between the two MPC schemes and FHE. This work provides system-level insights for researchers and practitioners who seek to understand or accelerate PPML workloads. Comments: ISPASS 2026 Accepted Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2604.00169 [cs.CR]   (or arXiv:2604.00169v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.00169 Focus to learn more Submission history From: Pengzhi Huang [view email] [v1] Tue, 31 Mar 2026 19:18:52 UTC (3,046 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 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?)