No Cover Image

Journal article 22 views 8 downloads

A Generic Cryptographic Deep-Learning Inference Platform for Remote Sensing Scenes

Qian Chen Orcid Logo, Yulin Wu Orcid Logo, Xuan Wang Orcid Logo, Zoe L. Jiang Orcid Logo, Weizhe Zhang Orcid Logo, Yang Liu Orcid Logo, Mamoun Alazab

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Volume: 16, Pages: 3309 - 3321

Swansea University Author: Yang Liu Orcid Logo

  • 67393.VoR.pdf

    PDF | Version of Record

    This work is licensed under a Creative Commons Attribution 4.0 License.

    Download (3.31MB)

Check full text

DOI (Published version): 10.1109/jstars.2023.3260867

Abstract

Deep learning plays an essential role in multidisciplinary research of remote sensing. We will encounter security problems during the data acquisition, processing, and result generation stages. Therefore, secure deep-learning inference services are one of the most important links. Some theoretical p...

Full description

Published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
ISSN: 1939-1404 2151-1535
Published: Institute of Electrical and Electronics Engineers (IEEE) 2024
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa67393
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2024-09-20T13:24:39Z
last_indexed 2024-09-20T13:24:39Z
id cronfa67393
recordtype SURis
fullrecord <?xml version="1.0" encoding="utf-8"?><rfc1807 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"><bib-version>v2</bib-version><id>67393</id><entry>2024-08-15</entry><title>A Generic Cryptographic Deep-Learning Inference Platform for Remote Sensing Scenes</title><swanseaauthors><author><sid>ba37dab58c9093dc63c79001565b75d4</sid><ORCID>0000-0003-2486-5765</ORCID><firstname>Yang</firstname><surname>Liu</surname><name>Yang Liu</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2024-08-15</date><deptcode>MACS</deptcode><abstract>Deep learning plays an essential role in multidisciplinary research of remote sensing. We will encounter security problems during the data acquisition, processing, and result generation stages. Therefore, secure deep-learning inference services are one of the most important links. Some theoretical progress has been made in cryptographic deep-learning inference, but it lacks a general platform that can be realized in reality. Constantly modifying the corresponding models to approximate the plaintext results reveal the model information to a certain extent. This article proposes a generic post-quantum platform named the PyHENet, which perfectly combines cryptography with plaintext deep learning libraries. Second, we optimize the convolution, activation, and pooling functions and complete the ciphertext operation under floating point numbers for the first time. Moreover, the computation process is accelerated by single instruction multiple data streams and GPU parallel computing. The experimental results show that the PyHENet is closer to the plaintext inference platform than any other cryptographic model and has satisfactory robustness. The optimized PyHENet obtained a better accuracy of 95.05% in the high-resolution NaSC-TG2 database, which the Tiangong-2 space station received.</abstract><type>Journal Article</type><journal>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</journal><volume>16</volume><journalNumber/><paginationStart>3309</paginationStart><paginationEnd>3321</paginationEnd><publisher>Institute of Electrical and Electronics Engineers (IEEE)</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>1939-1404</issnPrint><issnElectronic>2151-1535</issnElectronic><keywords>Convolutional neural network (CNN), deep learning inference, fully homomorphic encryption (HE), privacy preserving, remote sensing scenes</keywords><publishedDay>7</publishedDay><publishedMonth>4</publishedMonth><publishedYear>2024</publishedYear><publishedDate>2024-04-07</publishedDate><doi>10.1109/jstars.2023.3260867</doi><url/><notes/><college>COLLEGE NANME</college><department>Mathematics and Computer Science School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MACS</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>. This work was supported in part by the Basic Research Project of Shenzhen, China under Grant JCYJ20200109113405927 and Grant JCYJ20200109113427092, in part by the National Natural Science Foundation of China under Grant 61872109, Grant 62272131, and 62203134, in part by the National Science and Technology Major Project Carried on by Shenzhen under Grant CJGJZD20200617103000001, in part by the Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies under Grant 2022B1212010005, in part by the Science and Technology Project of Guangzhou under Grant 2020A1515010652, in part by the Key Fields R&amp;D Project of Guangdong Province under Grant 2020B0101380001, and in part by the PINGAN-HITsz Intelligence Finance Research Center. (Corresponding authors: Xuan Wang; Zoe L. Jiang.)</funders><projectreference/><lastEdited>2024-09-20T14:26:18.5279608</lastEdited><Created>2024-08-15T17:02:41.9710434</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Qian</firstname><surname>Chen</surname><orcid>0000-0002-2341-2118</orcid><order>1</order></author><author><firstname>Yulin</firstname><surname>Wu</surname><orcid>0000-0001-7952-7136</orcid><order>2</order></author><author><firstname>Xuan</firstname><surname>Wang</surname><orcid>0000-0002-3512-0649</orcid><order>3</order></author><author><firstname>Zoe L.</firstname><surname>Jiang</surname><orcid>0000-0002-8944-7444</orcid><order>4</order></author><author><firstname>Weizhe</firstname><surname>Zhang</surname><orcid>0000-0003-4783-876x</orcid><order>5</order></author><author><firstname>Yang</firstname><surname>Liu</surname><orcid>0000-0003-2486-5765</orcid><order>6</order></author><author><firstname>Mamoun</firstname><surname>Alazab</surname><order>7</order></author></authors><documents><document><filename>67393__31417__ac1bbe24aab44032b63d6e85145f9fb8.pdf</filename><originalFilename>67393.VoR.pdf</originalFilename><uploaded>2024-09-20T14:25:03.6356172</uploaded><type>Output</type><contentLength>3474076</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>This work is licensed under a Creative Commons Attribution 4.0 License.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling v2 67393 2024-08-15 A Generic Cryptographic Deep-Learning Inference Platform for Remote Sensing Scenes ba37dab58c9093dc63c79001565b75d4 0000-0003-2486-5765 Yang Liu Yang Liu true false 2024-08-15 MACS Deep learning plays an essential role in multidisciplinary research of remote sensing. We will encounter security problems during the data acquisition, processing, and result generation stages. Therefore, secure deep-learning inference services are one of the most important links. Some theoretical progress has been made in cryptographic deep-learning inference, but it lacks a general platform that can be realized in reality. Constantly modifying the corresponding models to approximate the plaintext results reveal the model information to a certain extent. This article proposes a generic post-quantum platform named the PyHENet, which perfectly combines cryptography with plaintext deep learning libraries. Second, we optimize the convolution, activation, and pooling functions and complete the ciphertext operation under floating point numbers for the first time. Moreover, the computation process is accelerated by single instruction multiple data streams and GPU parallel computing. The experimental results show that the PyHENet is closer to the plaintext inference platform than any other cryptographic model and has satisfactory robustness. The optimized PyHENet obtained a better accuracy of 95.05% in the high-resolution NaSC-TG2 database, which the Tiangong-2 space station received. Journal Article IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 16 3309 3321 Institute of Electrical and Electronics Engineers (IEEE) 1939-1404 2151-1535 Convolutional neural network (CNN), deep learning inference, fully homomorphic encryption (HE), privacy preserving, remote sensing scenes 7 4 2024 2024-04-07 10.1109/jstars.2023.3260867 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University . This work was supported in part by the Basic Research Project of Shenzhen, China under Grant JCYJ20200109113405927 and Grant JCYJ20200109113427092, in part by the National Natural Science Foundation of China under Grant 61872109, Grant 62272131, and 62203134, in part by the National Science and Technology Major Project Carried on by Shenzhen under Grant CJGJZD20200617103000001, in part by the Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies under Grant 2022B1212010005, in part by the Science and Technology Project of Guangzhou under Grant 2020A1515010652, in part by the Key Fields R&D Project of Guangdong Province under Grant 2020B0101380001, and in part by the PINGAN-HITsz Intelligence Finance Research Center. (Corresponding authors: Xuan Wang; Zoe L. Jiang.) 2024-09-20T14:26:18.5279608 2024-08-15T17:02:41.9710434 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Qian Chen 0000-0002-2341-2118 1 Yulin Wu 0000-0001-7952-7136 2 Xuan Wang 0000-0002-3512-0649 3 Zoe L. Jiang 0000-0002-8944-7444 4 Weizhe Zhang 0000-0003-4783-876x 5 Yang Liu 0000-0003-2486-5765 6 Mamoun Alazab 7 67393__31417__ac1bbe24aab44032b63d6e85145f9fb8.pdf 67393.VoR.pdf 2024-09-20T14:25:03.6356172 Output 3474076 application/pdf Version of Record true This work is licensed under a Creative Commons Attribution 4.0 License. true eng https://creativecommons.org/licenses/by/4.0/
title A Generic Cryptographic Deep-Learning Inference Platform for Remote Sensing Scenes
spellingShingle A Generic Cryptographic Deep-Learning Inference Platform for Remote Sensing Scenes
Yang Liu
title_short A Generic Cryptographic Deep-Learning Inference Platform for Remote Sensing Scenes
title_full A Generic Cryptographic Deep-Learning Inference Platform for Remote Sensing Scenes
title_fullStr A Generic Cryptographic Deep-Learning Inference Platform for Remote Sensing Scenes
title_full_unstemmed A Generic Cryptographic Deep-Learning Inference Platform for Remote Sensing Scenes
title_sort A Generic Cryptographic Deep-Learning Inference Platform for Remote Sensing Scenes
author_id_str_mv ba37dab58c9093dc63c79001565b75d4
author_id_fullname_str_mv ba37dab58c9093dc63c79001565b75d4_***_Yang Liu
author Yang Liu
author2 Qian Chen
Yulin Wu
Xuan Wang
Zoe L. Jiang
Weizhe Zhang
Yang Liu
Mamoun Alazab
format Journal article
container_title IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
container_volume 16
container_start_page 3309
publishDate 2024
institution Swansea University
issn 1939-1404
2151-1535
doi_str_mv 10.1109/jstars.2023.3260867
publisher Institute of Electrical and Electronics Engineers (IEEE)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description Deep learning plays an essential role in multidisciplinary research of remote sensing. We will encounter security problems during the data acquisition, processing, and result generation stages. Therefore, secure deep-learning inference services are one of the most important links. Some theoretical progress has been made in cryptographic deep-learning inference, but it lacks a general platform that can be realized in reality. Constantly modifying the corresponding models to approximate the plaintext results reveal the model information to a certain extent. This article proposes a generic post-quantum platform named the PyHENet, which perfectly combines cryptography with plaintext deep learning libraries. Second, we optimize the convolution, activation, and pooling functions and complete the ciphertext operation under floating point numbers for the first time. Moreover, the computation process is accelerated by single instruction multiple data streams and GPU parallel computing. The experimental results show that the PyHENet is closer to the plaintext inference platform than any other cryptographic model and has satisfactory robustness. The optimized PyHENet obtained a better accuracy of 95.05% in the high-resolution NaSC-TG2 database, which the Tiangong-2 space station received.
published_date 2024-04-07T14:26:16Z
_version_ 1810721697435746304
score 11.028798

Similar Items