Goten

GPU-Outsourcing Trusted Execution of Neural Network Training

A framework leveraging GPU and Intel SGX to protect privacy of training data, model, and queries while achieving high-performance training and prediction

The details of this project are presented in the following paper:

Goten: GPU-Outsourcing Trusted Execution of Neural Network Training
Lucien K. L. Ng, Sherman S. M. Chow, Anna P. Y. Woo, Donald P. H. Wong, Yongjun Zhao
To appear in AAAI-21

How to Install

Install Intel's linux-sgx and linux-sgx-driver. We tested our code on SGX SDK 2.6

Install PyTorch with Python3. You may install it using Anaconda

conda create -n goten python=3.6
conda install pytorch=1.2.0 torchvision cudatoolkit=10.0 -c pytorch

Make the C++ part of this repo
```
make -j4
```

How to run

Source your Intel SGK SDK environment. For example
```
source /opt/intel/sgxsdk/environment
```
Run VGG11

If you want to run all 3 non-colluding servers in a local machine, run the following command
```
python -m python.vgg
```
If the servers are distributed on different machines, please mark them as S0, S1, and S2, then
- S0 run the following command, where IPS0 is the IP address of S0
```
python -m python.vgg --ip="$IPS0" -s 0
```
- For S1
```
python -m python.vgg --ip="$IPS0" -s 1
```
- For S2
```
python -m python.vgg --ip="$IPS0" -s 2
```

CaffeSCONE

The guide of building and running CaffeSCONE is store in the folder named CaffeSCONE.

Disclaimer

DO NOT USE THIS SOFTWARE TO SECURE ANY REAL-WORLD DATA OR COMPUTATION!

This software is a proof-of-concept meant for performance testing of the Goten framework ONLY. It is full of security vulnerabilities that facilitate testing, debugging and performance measurements. In any real-world deployment, these vulnerabilities can be easily exploited to leak all user inputs.

Some parts that have a negligble impact on performance but that are required for a real-world deployment are not currently implemented (e.g., setting up a secure communication channel with a remote client and producing verifiable attestations).

Acknowledgement

we reuse some code of Slalom (Tramer & Boneh, 2019), including their code of crypgtographicially-secure random number generation and encryption/decryption, and their OS-call-free version of Eigen, a linear algebra library.

goten-team / Goten

readme