Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
PrivCirNet: Efficient Private Inference via Block Circulant Transformation
Authors: Tianshi Xu, Lemeng Wu, Runsheng Wang, Meng Li
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare Priv Cir Net with the stateof-the-art HE-based framework Bolt (IEEE S&P 2024) and HE-friendly pruning method Sp ENCNN (ICML 2023). For Res Net-18 and Vision Transformer (Vi T) on Tiny Image Net, Priv Cir Net reduces latency by 5.0 and 1.3 with iso-accuracy over Bolt, respectively, and improves accuracy by 4.1% and 12% over Sp ENCNN, respectively. |
| Researcher Affiliation | Collaboration | Tianshi Xu Peking University EMAIL Lemeng Wu Meta, Inc. lmwu@meta Runsheng Wang Peking University EMAIL Meng Li Peking University EMAIL |
| Pseudocode | Yes | Algorithm 1: Inverted Residual Fusion Algorithm |
| Open Source Code | Yes | Our code and checkpoints are available on Git Hub. |
| Open Datasets | Yes | We evaluate Priv Cir Net on Mobile Net V2 [28], Res Net-18 [55], and Vi T [29] across four datasets: CIFAR-10, CIFAR-100, Tiny Image Net and Image Net. |
| Dataset Splits | Yes | We evaluate Priv Cir Net on Mobile Net V2 [28], Res Net-18 [55], and Vi T [29] across four datasets: CIFAR-10, CIFAR-100, Tiny Image Net and Image Net. |
| Hardware Specification | Yes | All the experiments are performed on a machine with 2.4 GHz Intel Xeon CPU. For CIFAR and Tiny Image Net datasets, we train all models on a single NVIDIA RTX4090 GPU and a single NVIDIA A6000 GPU. For Image Net, we train all models on 8 NVIDIA A100 GPUs. |
| Software Dependencies | No | Priv Cir Net is built on top of the SEAL library [52] in C++. ... Following [7], we set n = 8192, security parameter λ = 128, plaintext bitwidth p = 41 and ciphertext bitwidth q = 218, which is also the default setting in SEAL library [52]. |
| Experiment Setup | Yes | Following [10, 53, 54], we simulate a LAN network setting via Linux Traffic Control, where the bandwidth is 384 MBps and the echo latency is 0.3ms. All the experiments are performed on a machine with 2.4 GHz Intel Xeon CPU. Following [7], we set n = 8192, security parameter λ = 128, plaintext bitwidth p = 41 and ciphertext bitwidth q = 218, which is also the default setting in SEAL library [52]. ... All baseline methods and Priv Cir Net are trained using identical hyper-parameters, including data augmentation, number of epochs, and others. These hyper-parameters are detailed in the configs folder within our codebase. |