Ironman: Accelerating Oblivious Transfer Extension for Privacy-Preserving AI with Near-Memory Processing

2025/07/22 by Lin, Chenqi, Kang Yang, Tianshi Xu +14
Computer Science · Engineering · #Cryptography and Data Security #FOS: Computer and information sciences #Ferroelectric and Negative Capacitance Devices #Hardware Architecture (cs.AR) #Security and Verification in Computing

paper · pdf · doi:10.48550/arxiv.2507.16391

openalex publication_date 2025/07/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

With the wide application of machine learning (ML), privacy concerns arise with user data as they may contain sensitive information. Privacy-preserving ML (PPML) based on cryptographic primitives has emerged as a promising solution in which an ML model is directly computed on the encrypted data to provide a formal privacy guarantee. However, PPML frameworks heavily rely on the oblivious transfer (OT) primitive to compute nonlinear functions. OT mainly involves the computation of single-point correlated OT (SPCOT) and learning parity with noise (LPN) operations. As OT is still computed extensively on general-purpose CPUs, it becomes the latency bottleneck of modern PPML frameworks. In this paper, we propose a novel OT accelerator, dubbed Ironman, to significantly increase the efficiency of OT and the overall PPML framework. We observe that SPCOT is computation-bounded, and thus propose a hardware-friendly SPCOT algorithm with a customized accelerator to improve SPCOT computation throughput. In contrast, LPN is memory-bandwidth-bounded due to irregular memory access patterns. Hence, we further leverage the near-memory processing (NMP) architecture equipped with memory-side cache and index sorting to improve effective memory bandwidth. With extensive experiments, we demonstrate Ironman achieves a 39.2-237.4 times improvement in OT throughput across different NMP configurations compared to the full-thread CPU implementation. For different PPML frameworks, Ironman demonstrates a 2.1-3.4 times reduction in end-to-end latency for both CNN and Transformer models.

Citations

• LLaMA: Open and Efficient Foundation Language Models
• GPU-based Private Information Retrieval for On-Device Machine Learning Inference
• SIRNN: A Math Library for Secure RNN Inference
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing
• QUOTIENT: Two-Party Secure Neural Network Training and Prediction
• XONN: XNOR-based Oblivious Deep Neural Network Inference
• Gazelle: A Low Latency Framework for Secure Neural Network Inference
• Densely Connected Convolutional Networks
• Deep Residual Learning for Image Recognition
• Noise-Tolerant Learning, the Parity Problem, and the Statistical Query Model
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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