CNN encryption using XOR Gate for Hardware Optimization

Ki Beom Lee; Sumin Lee; Sunghwan Joo; Hong Keun Ahn; Young Seok Jung; Seong Ook Jung

doi:10.1109/ISOCC53507.2021.9614013

CNN encryption using XOR Gate for Hardware Optimization

Ki Beom Lee, Sumin Lee, Sunghwan Joo, Hong Keun Ahn, Young Seok Jung, Seong Ook Jung

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

Artificial intelligence networks have been researched in many fields such as computer vision, health care, and military service. Convolutional neural network (CNN) is one of the basic neural networks that uses convolutional operations as the basis to train data and perform desired application. However, important parameters used in CNN applications suffer from security issues. Thus, the need for data protection is increasing. The traditional approach of storing training data and weight parameters encrypted in external memory has the disadvantage of large hardware area. In this paper, the CNN-based encrypting method using XOR gates is proposed to reduce hardware resources by allowing encryption and decryption to operate in a single module. The proposed encrypting method reduces hardware usage by more than 100 times compared to the conventional encrypting method.

Original language	English
Title of host publication	Proceedings - International SoC Design Conference 2021, ISOCC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	359-360
Number of pages	2
ISBN (Electronic)	9781665401746
DOIs	https://doi.org/10.1109/ISOCC53507.2021.9614013
Publication status	Published - 2021
Event	18th International System-on-Chip Design Conference, ISOCC 2021 - Jeju Island, Korea, Republic of Duration: 2021 Oct 6 → 2021 Oct 9

Publication series

Name	Proceedings - International SoC Design Conference 2021, ISOCC 2021

Conference

Conference	18th International System-on-Chip Design Conference, ISOCC 2021
Country/Territory	Korea, Republic of
City	Jeju Island
Period	21/10/6 → 21/10/9

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Information Systems
Hardware and Architecture
Electrical and Electronic Engineering

Access to Document

10.1109/ISOCC53507.2021.9614013

Cite this

Lee, K. B., Lee, S., Joo, S., Ahn, H. K., Jung, Y. S., & Jung, S. O. (2021). CNN encryption using XOR Gate for Hardware Optimization. In Proceedings - International SoC Design Conference 2021, ISOCC 2021 (pp. 359-360). (Proceedings - International SoC Design Conference 2021, ISOCC 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISOCC53507.2021.9614013

@inproceedings{0e2dad60649844659be39902fab565f9,

title = "CNN encryption using XOR Gate for Hardware Optimization",

abstract = "Artificial intelligence networks have been researched in many fields such as computer vision, health care, and military service. Convolutional neural network (CNN) is one of the basic neural networks that uses convolutional operations as the basis to train data and perform desired application. However, important parameters used in CNN applications suffer from security issues. Thus, the need for data protection is increasing. The traditional approach of storing training data and weight parameters encrypted in external memory has the disadvantage of large hardware area. In this paper, the CNN-based encrypting method using XOR gates is proposed to reduce hardware resources by allowing encryption and decryption to operate in a single module. The proposed encrypting method reduces hardware usage by more than 100 times compared to the conventional encrypting method.",

keywords = "CNN, Encryption, Machine learning, Security, XOR cipher",

author = "Lee, {Ki Beom} and Sumin Lee and Sunghwan Joo and Ahn, {Hong Keun} and Jung, {Young Seok} and Jung, {Seong Ook}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 18th International System-on-Chip Design Conference, ISOCC 2021 ; Conference date: 06-10-2021 Through 09-10-2021",

year = "2021",

doi = "10.1109/ISOCC53507.2021.9614013",

language = "English",

series = "Proceedings - International SoC Design Conference 2021, ISOCC 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "359--360",

booktitle = "Proceedings - International SoC Design Conference 2021, ISOCC 2021",

address = "United States",

}

Lee, KB, Lee, S, Joo, S, Ahn, HK, Jung, YS & Jung, SO 2021, CNN encryption using XOR Gate for Hardware Optimization. in Proceedings - International SoC Design Conference 2021, ISOCC 2021. Proceedings - International SoC Design Conference 2021, ISOCC 2021, Institute of Electrical and Electronics Engineers Inc., pp. 359-360, 18th International System-on-Chip Design Conference, ISOCC 2021, Jeju Island, Korea, Republic of, 21/10/6. https://doi.org/10.1109/ISOCC53507.2021.9614013

CNN encryption using XOR Gate for Hardware Optimization. / Lee, Ki Beom; Lee, Sumin; Joo, Sunghwan et al.
Proceedings - International SoC Design Conference 2021, ISOCC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 359-360 (Proceedings - International SoC Design Conference 2021, ISOCC 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - CNN encryption using XOR Gate for Hardware Optimization

AU - Lee, Ki Beom

AU - Lee, Sumin

AU - Joo, Sunghwan

AU - Ahn, Hong Keun

AU - Jung, Young Seok

AU - Jung, Seong Ook

PY - 2021

Y1 - 2021

N2 - Artificial intelligence networks have been researched in many fields such as computer vision, health care, and military service. Convolutional neural network (CNN) is one of the basic neural networks that uses convolutional operations as the basis to train data and perform desired application. However, important parameters used in CNN applications suffer from security issues. Thus, the need for data protection is increasing. The traditional approach of storing training data and weight parameters encrypted in external memory has the disadvantage of large hardware area. In this paper, the CNN-based encrypting method using XOR gates is proposed to reduce hardware resources by allowing encryption and decryption to operate in a single module. The proposed encrypting method reduces hardware usage by more than 100 times compared to the conventional encrypting method.

AB - Artificial intelligence networks have been researched in many fields such as computer vision, health care, and military service. Convolutional neural network (CNN) is one of the basic neural networks that uses convolutional operations as the basis to train data and perform desired application. However, important parameters used in CNN applications suffer from security issues. Thus, the need for data protection is increasing. The traditional approach of storing training data and weight parameters encrypted in external memory has the disadvantage of large hardware area. In this paper, the CNN-based encrypting method using XOR gates is proposed to reduce hardware resources by allowing encryption and decryption to operate in a single module. The proposed encrypting method reduces hardware usage by more than 100 times compared to the conventional encrypting method.

KW - CNN

KW - Encryption

KW - Machine learning

KW - Security

KW - XOR cipher

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ER -

CNN encryption using XOR Gate for Hardware Optimization

Abstract

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Bibliographical note

All Science Journal Classification (ASJC) codes

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