zkCNN
Introduction
This is the implementation of this paper, which is a GKRbased zeroknowledge proof for CNN reference, containing some common CNN models such as LeNet5, vgg11 and vgg16.
Requirement
 C++14
 cmake >= 3.10
 GMP library
Input Format
The input has two part which are data and weight in the matrix.
Data Part
This part is the picture data, a vector reshaped from its original matrix by
where is the number of channel, is the height, is the width.
Weight Part
This part is the set of parameters in the neural network, which contains

where and are the number of output and input channels, is the sideness of the kernel (here we only support square kernel).
Experiment Script
Clone the repo
To run the code, make sure you clone with
git clone recursesubmodules [email protected]:TAMUCrypto/zkCNN.git
since the polynomial commitment is included as a submodule.
Run a demo of LeNet5
The script to run LeNet5 model (please run the script in script/
directory).
./demo_lenet.sh
 The input data is in
data/lenet5.mnist.relu.max/
.  The experiment evaluation is
output/single/demoresultlenet5.txt
.  The inference result is
output/single/lenet5.mnist.relu.max1infer.csv
.
Run a demo of vgg11
The script to run vgg11 model (please run the script in script/
directory).
./demo_vgg.sh
 The input data is in
data/vgg11/
.  The experiment evaluation is
output/single/demoresult.txt
.  The inference result is
output/single/vgg11.cifar.relu1infer.csv
.
Polynomial Commitment
Here we implement a hyrax polynomial commitment based on BLS12381 elliptic curve. It is a submodule and someone who is interested can refer to this repo hyraxbls12381.
Reference

zkCNN: Zero knowledge proofs for convolutional neural network predictions and accuracy. Liu, T., Xie, X., & Zhang, Y. (CCS 2021).

Doublyefficient zksnarks without trusted setup. Wahby, R. S., Tzialla, I., Shelat, A., Thaler, J., & Walfish, M. (S&P 2018)