Compression and Cryptography Algorithms for 3D Remote Sensing Point Cloud Data Based on 3D-TCICM and ECC

This paper proposes an asymmetric encryption and compression method based on three-dimensional Tent-Cubic-ICMIC map (3D-TCICM) and elliptic curve encryption (ECC) for 3D remote sensing point cloud. Due to the large size, rich data and high security of remote sensing point cloud, an encryption and co...

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Main Authors: Pengfei Yan, Shixian Nan, Xiufang Feng, Yongfei Wu, Jie Yang, Hao Zhang
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Photonics Journal
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10250950/
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author Pengfei Yan
Shixian Nan
Xiufang Feng
Yongfei Wu
Jie Yang
Hao Zhang
author_facet Pengfei Yan
Shixian Nan
Xiufang Feng
Yongfei Wu
Jie Yang
Hao Zhang
author_sort Pengfei Yan
collection DOAJ
description This paper proposes an asymmetric encryption and compression method based on three-dimensional Tent-Cubic-ICMIC map (3D-TCICM) and elliptic curve encryption (ECC) for 3D remote sensing point cloud. Due to the large size, rich data and high security of remote sensing point cloud, an encryption and compression method with high computing efficiency and high security is required. The point cloud is first divided into blocks, then compressed by point cloud library (PCL) using octree, and then the resulting data stream is encrypted. During encryption, a chaotic system 3D-TCICM with excellent chaotic behavior is proposed. Its initial values are generated by point cloud data and ECC algorithm. The sender and receiver only hold the public key or private key respectively, which further improves the security of the algorithm. The encryption factors are also generated for subsequent encryption operations, which improves the plaintext correlation. The encryption algorithm includes innovative scrambling algorithm between multiple blocks (MBS), helix diffusion in GF(<inline-formula><tex-math notation="LaTeX">$2^{8}$</tex-math></inline-formula>) field and multiple S-boxes substitution, in which different S-boxes are provided for each data block. Simulation results indicate that the proposed scheme surpasses existing algorithms in terms of security, randomness, and plaintext correlation, all while maintaining a lower computational complexity. The algorithm exhibits robustness against various attacks such as differential, statistical, chosen-plaintext, known-plaintext, chosen-ciphertext, noise, and cropping attacks.
format Article
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institution Kabale University
issn 1943-0655
language English
publishDate 2023-01-01
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record_format Article
series IEEE Photonics Journal
spelling doaj-art-9d0a1f2a43d6478f96184822111737172025-08-20T03:30:57ZengIEEEIEEE Photonics Journal1943-06552023-01-0115512310.1109/JPHOT.2023.331484010250950Compression and Cryptography Algorithms for 3D Remote Sensing Point Cloud Data Based on 3D-TCICM and ECCPengfei Yan0Shixian Nan1Xiufang Feng2Yongfei Wu3Jie Yang4Hao Zhang5https://orcid.org/0000-0003-4281-1461College of Software, Taiyuan University of Technology, Jinzhong, ChinaCollege of Software, Taiyuan University of Technology, Jinzhong, ChinaCollege of Software, Taiyuan University of Technology, Jinzhong, ChinaCollege of Computer, Data Science, Taiyuan University of Technology, Jinzhong, ChinaCollege of Software, Taiyuan University of Technology, Jinzhong, ChinaCollege of Computer, Data Science, Taiyuan University of Technology, Jinzhong, ChinaThis paper proposes an asymmetric encryption and compression method based on three-dimensional Tent-Cubic-ICMIC map (3D-TCICM) and elliptic curve encryption (ECC) for 3D remote sensing point cloud. Due to the large size, rich data and high security of remote sensing point cloud, an encryption and compression method with high computing efficiency and high security is required. The point cloud is first divided into blocks, then compressed by point cloud library (PCL) using octree, and then the resulting data stream is encrypted. During encryption, a chaotic system 3D-TCICM with excellent chaotic behavior is proposed. Its initial values are generated by point cloud data and ECC algorithm. The sender and receiver only hold the public key or private key respectively, which further improves the security of the algorithm. The encryption factors are also generated for subsequent encryption operations, which improves the plaintext correlation. The encryption algorithm includes innovative scrambling algorithm between multiple blocks (MBS), helix diffusion in GF(<inline-formula><tex-math notation="LaTeX">$2^{8}$</tex-math></inline-formula>) field and multiple S-boxes substitution, in which different S-boxes are provided for each data block. Simulation results indicate that the proposed scheme surpasses existing algorithms in terms of security, randomness, and plaintext correlation, all while maintaining a lower computational complexity. The algorithm exhibits robustness against various attacks such as differential, statistical, chosen-plaintext, known-plaintext, chosen-ciphertext, noise, and cropping attacks.https://ieeexplore.ieee.org/document/10250950/3D point cloudasymmetric encryptioncompressionhyperchaotic systemremote sensing
spellingShingle Pengfei Yan
Shixian Nan
Xiufang Feng
Yongfei Wu
Jie Yang
Hao Zhang
Compression and Cryptography Algorithms for 3D Remote Sensing Point Cloud Data Based on 3D-TCICM and ECC
IEEE Photonics Journal
3D point cloud
asymmetric encryption
compression
hyperchaotic system
remote sensing
title Compression and Cryptography Algorithms for 3D Remote Sensing Point Cloud Data Based on 3D-TCICM and ECC
title_full Compression and Cryptography Algorithms for 3D Remote Sensing Point Cloud Data Based on 3D-TCICM and ECC
title_fullStr Compression and Cryptography Algorithms for 3D Remote Sensing Point Cloud Data Based on 3D-TCICM and ECC
title_full_unstemmed Compression and Cryptography Algorithms for 3D Remote Sensing Point Cloud Data Based on 3D-TCICM and ECC
title_short Compression and Cryptography Algorithms for 3D Remote Sensing Point Cloud Data Based on 3D-TCICM and ECC
title_sort compression and cryptography algorithms for 3d remote sensing point cloud data based on 3d tcicm and ecc
topic 3D point cloud
asymmetric encryption
compression
hyperchaotic system
remote sensing
url https://ieeexplore.ieee.org/document/10250950/
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