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..
Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data
Authors: Qian Lou, Bo Feng, Geoffrey Charles Fox, Lei Jiang
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results show Glyph obtains state-of-the-art accuracy, and reduces training latency by 69% 99% over prior FHE-based privacy-preserving techniques on encrypted datasets. |
| Researcher Affiliation | Academia | Qian Lou EMAIL Bo Feng EMAIL Geoffrey C. Fox EMAIL Lei Jiang EMAIL Indiana University Bloomington |
| Pseudocode | No | The paper describes methods through textual explanation and diagrams, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | Our encrypted datasets include MNIST [22] and Skin-Cancer-MNIST [20]. ... We also used SVHN [23] and CIFAR-10 [24] to pre-train our models which are for transfer learning on encrypted datasets. |
| Dataset Splits | No | Skin-Cancer-MNIST consists of 10015 dermatoscopic images and includes a representative collection of 7 important diagnostic categories in the realm of pigmented lesions. We grouped it into a 8K training dataset and a 2K test dataset. |
| Hardware Specification | Yes | We evaluated all schemes on an Intel Xeon E78890 v4 2.2GHz CPU with 256GB DRAM. It has two sockets, each of which owns 12 cores and supports 24 threads. |
| Software Dependencies | No | The paper mentions using the HElib [7] library and the TFHE [9] library, but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We adopted two network architectures, a 3-layer MLP [2] and a 4-layer CNN shown in Figure 4. ... We quantized the inputs, weights and activations of two network architectures with 8-bit by the training quantization technique in SWALP [25]. For BGV, we used the same parameter setting rule as [21]... We set the parameters of TFHE to the same security level as BGV... For first-level TLWE, we set the minimal noise standard variation to α = 6.10 10 5 and the count of coefficients to n = 280 to achieve the security level of λ = 80. |