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3D Point Cloud Encryption Through Chaotic Mapping

  • Xin Jin
  • Zhaoxing Wu
  • Chenggen Song
  • Chunwei Zhang
  • Xiaodong Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9916)

Abstract

Three dimensional (3D) contents such as 3D point clouds, 3D meshes and 3D surface models are increasingly growing and being widely spread into the industry and our daily life. However, less people consider the problem of the privacy preserving of 3D contents. As an attempt towards 3D security, in this papers, we propose methods of encrypting the 3D point clouds through chaotic mapping. 2 schemes of encryption using chaotic mapping have been proposed to encrypt 3D point clouds. (1) 3 random sequences are generated by the logistic chaotic mapping. Each random vector is sorted to randomly shuffler each coordinate of the 3D point clouds. (2) A random 3×3 invertible rotation matrix and a 3×1 translate vector are generated by the logistic mapping. Then each 3D point is projected to another random place using the above random rotation matrix and translate vector in the homogeneous coordinate. We test the above 2 encryption schemes of 3D point cloud encryption using various 3D point clouds. The 3D point clouds can be encrypted and decrypted correctly. In addition, we evaluated the encryption results by VFH (Viewpoint Feature Histogram). The experimental results show that our proposed methods can produce nearly un-recognized encrypted results of 3D point clouds.

Keywords

3D point clouds Encryption Chaotic mapping Point feature histogram View feature histogram 

Notes

Acknowledgement

The work is supported by the National Natural Science Foundation of China (No. 61402021), the Science and Technology Program of the State Archives Administration (2015-B-10), and the open funding project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (Grant No. BUAA-VR-16KF-09).

References

  1. 1.
    Huang, C., Nien, H.: Multi chaotic systems based pixel shuffle for image encryption. Opt. Commun. 282, 2123–2127 (2009)CrossRefGoogle Scholar
  2. 2.
    Lian, S., Sun, J., Wang, Z.: A block cipher based on a suitable use of the chaotic standard map. Chaos Soliton. Fract. 26(1), 117–129 (2005)CrossRefzbMATHGoogle Scholar
  3. 3.
    Zhen, P., Zhao, G., Min, L.Q., Jin, X.: Chaos-based image encryption scheme combining DNA coding and entropy. Multimedia Tools Appl. (MTA) 75, 6303–6319 (2015)CrossRefGoogle Scholar
  4. 4.
    Wang YZ., Ren GY., Jiang JL., Zhang J., Sun L.J.: Image encryption method based on chaotic map. In: 2nd IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 2558–2560 (2007)Google Scholar
  5. 5.
    Jin, X., et al.: Private video foreground extraction through chaotic mapping based encryption in the cloud. In: Tian, Q., Sebe, N., Qi, G.-J., Huet, B., Hong, R., Liu, X. (eds.) MMM 2016. LNCS, vol. 9516, pp. 562–573. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-27671-7_47 CrossRefGoogle Scholar
  6. 6.
    Jin, X., Tian, Y., Song, C., Wei, G., Li, X., Zhao, G., Wang, H.: An invertible and anti-chosen plaintext attack image encryption method based on DNA encoding and chaotic mapping. In: Chinese Automation Congress (CAC), Wuhan, China, 2015, pp. 11.27– 11.29 (2015)Google Scholar
  7. 7.
    Jin, X., Liu, Y., Li, X., Zhao, G., Chen, Y., Guo, K.: Privacy preserving face identification in the cloud through sparse representation. In: Yang, J., Yang, J., Sun, Z., Shan, S., Zheng, W., Feng, J. (eds.) Biometric Recognition. LNCS, vol. 9428, pp. 160–167. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-25417-3_20 CrossRefGoogle Scholar
  8. 8.
    Jin, X., Chen, Y., Ge, S., Zhang, K., Li, X., Li, Y., Liu, Y., Guo, K., Tian, Y., Zhao, G., Zhang, X., Wang, Z.: Color image encryption in CIE L*a*b* space. In: Niu, W., Li, G., Liu, J., Tan, J., Guo, L., Han, Z., Batten, L. (eds.) ATIS 2015. CCIS, vol. 557, pp. 74–85. Springer, Heidelberg (2015). doi: 10.1007/978-3-662-48683-2_8 CrossRefGoogle Scholar
  9. 9.
    Li, Y., et al.: An image encryption algorithm based on Zigzag transformation and 3-Dimension chaotic Logistic map. In: Niu, W., Li, G., Liu, J., Tan, J., Guo, L., Han, Z., Batten, L. (eds.) ATIS 2015. CCIS, vol. 557, pp. 3–13. Springer, Heidelberg (2015). doi: 10.1007/978-3-662-48683-2_1 CrossRefGoogle Scholar
  10. 10.
    del Rey, A.M.: A method to encrypt 3D solid objects based on three-dimensional cellular automata. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds.) HAIS 2015. LNCS (LNAI), vol. 9121, pp. 427–438. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-19644-2_36 CrossRefGoogle Scholar
  11. 11.
    Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: IEEE International Conference on Robotics and Automation (2009)Google Scholar
  12. 12.
    Éluard M., Maetz Y., Doërr G.: Geometry-preserving encryption for 3D meshes. In: Compression Et Représentation Des Signaux Audiovisuels (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Beijing Electronic Science and Technology InstituteBeijingChina
  2. 2.GOCPCCC Key Laboratory of Information SecurityBeijingChina

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