Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data
Authors: Qian Lou, Bo Feng, Geoffrey Charles Fox, Lei Jiang
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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 louqian@iu.edu Bo Feng fengbo@iu.edu Geoffrey C. Fox gcf@indiana.edu Lei Jiang jiang60@iu.edu 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. |