Official repository for UniHENN: Designing More Versatile Homomorphic Encryption-based CNNs without im2col
by Hyunmin Choi^1,2, Jihun Kim², Seungho Kim², Seonhye Park², Jeongyong Park^2,3, Wonbin Choi¹, and Hyoungshick Kim^2**

¹ NAVER Cloud, South Korea ² Sungkyunkwan University, South Korea ³ Samsung Electronics, South Korea

** Corresponding author

You can read the paper here: ArXiv Link

Designing Faster and More Versatile Homomorpic Encryption-based CNNs

UniHENN is a Python package that provides Homomorphic Encryption(HE)-based CNN inference. It provides the following features:

• HE-based CNN inference compatible with pytorch library
  • Automatically convert each layer to a compatible HE-based layer
  • Not only offers 2D-CNN, but also offers 1D CNN inference.
• It offers batch operation to optimize the inference time

Server Setting

• We use NAVER Cloud servers (https://www.ncloud.com/product/compute/server)
• In this scenario, we use the following server spec.
  • All server spec are Standard-g2 Server.
  • CentOS 7.8.64
  • 16 cores (Intel(R) Xeon(R) Gold 5220 CPU @ 2.20GHz) with 64GB DRAM

Requirements

• Python 3.8.1
• SEAL-Python (Release 4.0.0, https://github.com/Huelse/SEAL-Python)
• Pytorch
• TorchVision
• numpy
• pandas

How to use

If you want to test the M3 model, enter the following command in the shell:

python main.py 3

You can enter values from 1 to 6.

Dataset

We use MNIST and CIFAR10 datasets for 2D CNN models which can be downloaded using Torchvision. The details about datasets can be found at https://pytorch.org/vision/0.15/datasets.html.

HE Parameters

We use the Cheon-Kim-Kim-Song(CKKS) scheme following parameter settings:

Parameter	Value
# of slots	8,192
log Q	432
PK(MB)	1.87
SK(MB)	0.94
GK(GB)	0.57
RK(MB)	22.52
ctxt(MB)	1.68
# of mult(depth)	11

The PK, SK, GK, and RK mean the public key, secret key, galois key, and relinearization key each other. These parameter settings guarantee the 128-bit security parameters.

Models

We conducted seven experiments to test the performance of UniHENN with various models and data. The test file is in the uni_henn directory.

Model	Type	Dataset	# of layers	Test file
M1	2D CNN	MNIST	6	M1_test.py
M2	2D CNN	MNIST	8	M2_test.py
M3	2D CNN	MNIST	7	M3_test.py
M4	2D CNN	MNIST	12	M4_test.py
M5	2D CNN	Cifar-10	11	M5_test.py
M6	1D CNN	ECG Dataset	7	M6_test.py

Contributing

• Architecture Designer: Hyunmin Choi(https://github.com/hm-choi)
• Main Contributor: Jihun Kim (https://github.com/JihunSKKU), Hyunmin Choi(https://github.com/hm-choi)
• Contributor: Seungho Kim (https://github.com/Seungho-Kim-SKKU)
• Code Reviewer: Wonbin Choi (https://github.com/bindon)
• Register issues and pull requests are welcome. If there are some errors or changes then please open an issue and write down the details.

Publications

TBD

License

TBD