{"ID":2891724,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.16391","arxiv_id":"2507.16391","title":"Ironman: Accelerating Oblivious Transfer Extension for Privacy-Preserving AI with Near-Memory Processing","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.","short_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 priv...","url_abs":"https://arxiv.org/abs/2507.16391","url_pdf":"https://arxiv.org/pdf/2507.16391v3","authors":"[\"Chenqi Lin\",\"Kang Yang\",\"Tianshi Xu\",\"Ling Liang\",\"Yufei Wang\",\"Zhaohui Chen\",\"Runsheng Wang\",\"Mingyu Gao\",\"Meng Li\"]","published":"2025-07-22T09:35:59Z","proceeding":"cs.AR","tasks":"[\"cs.AR\"]","methods":"[\"Transformer\",\"Convolutional Neural Network\"]","has_code":false}