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

Abstract

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($2^{8}$) 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.

Keywords

• 3D point cloud
• asymmetric encryption
• compression
• hyperchaotic system
• remote sensing