Low Latency Privacy Preserving Inference
Authors: Alon Brutzkus, Ran Gilad-Bachrach, Oren Elisha
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the efficacy of our methods on several computer vision tasks. |
| Researcher Affiliation | Collaboration | 1Microsoft Research and Tel Aviv University, Israel 2Microsoft, Israel 3Microsoft Research, Israel. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. Descriptions of procedures are given in narrative text. |
| Open Source Code | Yes | Our code is freely available at https://github.com/microsoft/Crypto Nets. |
| Open Datasets | Yes | Here we present private predictions on the MNIST data-set (Le Cun et al., 2010)... The Cifar-10 data-set (Krizhevsky & Hinton, 2009)... Cal Tech-101 dataset (Fei-Fei et al., 2006). |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits needed to reproduce the experiment for all datasets. For Cal Tech-101, it mentions "the first 20 where used for training and the other 10 examples where used for testing", which is a train/test split, but no specific validation split is stated for any dataset. |
| Hardware Specification | Yes | On the reference machine used for this work (Azure standard B8ms virtual machine with 8 v CPUs and 32GB of RAM) |
| Software Dependencies | Yes | We use version 2.3.1 of the SEAL, http://sealcrypto. org/ |
| Experiment Setup | Yes | As a benchmark, we applied both to the same network that has accuracy of 98.95%. After suppressing adjacent linear layers it can be presented as a 5 5 convolution layer with a stride of (2, 2) and 5 output maps, which is followed by a square activation function that feeds a fully connected layer with 100 output neurons, another square activation and another fully connected layer with 10 outputs (in the supplementary material we include an image of the architecture). |