Summary

sgx-dnet is a port of the machine learning library Darknet into Intel SGX.
This library can be used to securely train and test neural networks models as well as perform inference on pre-trained models following the Darknet API.
sgx-dnet source is separated into two main parts: dnet-in (Enclave/dnet-in) and dnet-out (App/dnet-out). dnet-incontains all the necessary API to do secure training and inference within the enclave runtime, without any I/O functionality or system calls, and the dnet-out acts as a support library for the dnet-in and complements any unsupported enclave functionality.
Both parts communicate via ecalls and ocalls when/where necessary.
We redefine unsupported I/O calls like fread, fwrite etc, which act as wrapper functions for ocalls which invoke the corresponding libc routines in the untrusted runtime. These wrappers are mainly used for checkpointing and reading weights to/from disk respectively.
For performance reasons, other I/O functionality like reading training data and network config files are performed completely outside; developers using sgx-dnet should design their code in such a way that any non-sensitive input data is read into untrusted memory before proceeding into the enclave for training and inference. This style minimizes unecessary and expensive I/O during training or inference. For scenarios where training or inference data is to be kept private, the above mentioned wrapper functions could still be exploited.
OPenCV functionality and video processing are not supported as of now. Future versions may take those into account.

Training a model

To train a model, add a routine in App.cpp similar to the example cifar trainer: train_mnist.
Create a corresponding trainer routine in the trusted side which will be called via the ecall_trainer ecall.
Read and parse the model config file into a list data structure in the untrusted runtime.
Read the training data into the global training_data object/variable.
Perform an ecall with the list and training data objects; the secure training routine is performed within the ecall.
Modify Trainer.edl or Enclave.edl accordingly if you need to add more e/ocalls.
To test the mnist training and testing, download the 4 mnist training and test data and labels here:mnist data, decompress the files and add them to the App/dnet-out/data/mnist folder before launching the training and test routines.
See the example trainer with the mnist model for more inspiration on how to train and test other models.

Saving/Checkpointing networks during training

Network weights can be saved to disk during training via the save_weights(net,path) API.
This routine leverages the file i/o wrappers described above.

Testing the model

To test the model after training, add a routine in App.cpp which takes the test data as input.
Add a test routine in the enclave/trusted section which performs inference on a trained network object. This object could reside in enclave memory or be created from a weights file.
Perform an ecall into the enclave runtime with the test data, and run your test routine within the enclave.
Sample output: testing trained mnist model.

Doing inference

By providing a weight file and corresponding labels, you can perform inference on a pre-trained network model.
The tiny darknet classification example shows how to classify images using a pre-trained network.
The trained model weights can be obtained via: wget https://pjreddie.com/media/files/tiny.weights. Copy these weights to the Apps/dnet-out/backup folder and modify the corresponding path to the weights file in trainer.c. Equally modify the paths to the corresponding config file and test image in App.cpp.
Run the test_tiny routine in the main function.
Sample top5 prediction on the eagle image:

Debug hints

The sgx-gdb debug tool is recommended for debugging your enclave application.
In case you have "strange" seg faults, your neural network may be too large to fit in the enclave heap.
Try increasing the enclave heap size i.e the HeapMaxSize parameter in the enclave config file. It is 4GB (0x100000000) by default in this project.

Note

All CUDA functionality is disabled/stripped off in dnet-in because GPUs do not have access to enclave memory.
The secure dnet-in library contributes approximately 2MB to the total enclave size after build.

For questions or issues regarding sgx-dnet please contact me: xxxxxx@gmail.com.

Darknet

Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.

For more information see the Darknet project website.

sandeep007734/sgx-dnet