An Intra-slice Chaotic-Based Security Solution for Privacy Preservation in Future 5G Systems

  • Pilar Mareca
  • Borja Bordel
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 746)


The great heterogeneity of applications supported by future 5G mobile systems makes very difficult to imagine how an uniform network solution may satisfy in an efficient way all user requirements. Thus, several authors have proposed the idea of network slicing, a technique where network resources are packaged and assigned in an isolated manner to set of users according to their specific requirements. In this context, different slices for IoT systems, eHealth applications or standard mobile communications have been defined. For each slice, specific intra-slice solutions for device management, security provision, and other important pending challenges must be investigated and proposed. Therefore, in this paper an intra-slice chaotic-based security solution for privacy preservation is described. The presented solution employs various mathematical procedures to transform the three chaotic signals of Lorenz dynamics into three binary flows, employed to cipher and mask the private information, using a reduced resource microcontroller. A first implementation of the proposed system is also described in order to validate the described solution.


5G Network slicing Security Chaos CDMA systems Cryptography 



Borja Bordel has received funding from the Ministry of Education through the FPU program (grant number FPU15/03977) and from the Ministry of Economy and Competitiveness through SEMOLA project (TEC2015-68284-R).


  1. 1.
    Bordel, B., Alcarria, R., Robles, T., Martín, D.: Cyber–physical systems: extending pervasive sensing from control theory to the Internet of Things. Pervasive Mob. Comput. 40, 156–184 (2017)CrossRefGoogle Scholar
  2. 2.
    Sánchez, B.B., Sánchez-Picot, Á., De Rivera, D.S.: Using 5G technologies in the Internet of Things handovers, problems and challenges. In: 9th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), pp. 364–369. IEEE, July 2015Google Scholar
  3. 3.
    Zhang, H., Liu, N., Chu, X., Long, K., Aghvami, A., Leung, V.C.M.: Network slicing based 5G and future mobile networks: mobility, resource management, and challenges (2017). arXiv preprint arXiv:1704.07038CrossRefGoogle Scholar
  4. 4.
    Foukas, X., Patounas, G., Elmokashfi, A., Marina, M.K.: Network slicing in 5G: survey and challenges. IEEE Commun. Mag. 55(5), 94–100 (2017)CrossRefGoogle Scholar
  5. 5.
    Aggarwal, C.C., Philip, S.Y.: A general survey of privacy-preserving data mining models and algorithms. In: Privacy-Preserving Data Mining, pp. 11–52. Springer, Boston (2008)CrossRefGoogle Scholar
  6. 6.
    Wu, X., Ying, X., Liu, K., Chen, L.: A survey of privacy-preservation of graphs and social networks. In: Managing and Mining Graph Data, pp. 421–453 (2010)CrossRefGoogle Scholar
  7. 7.
    Zhou, B., Pei, J., Luk, W.: A brief survey on anonymization techniques for privacy preserving publishing of social network data. ACM SIGKDD Explor. Newsl. 10(2), 12–22 (2008)CrossRefGoogle Scholar
  8. 8.
    Bertino, E., Lin, D., Jiang, W.: A survey of quantification of privacy preserving data mining algorithms. In: Privacy-Preserving Data Mining, pp. 183–205. Springer, Boston (2008)CrossRefGoogle Scholar
  9. 9.
    Fung, B., Wang, K., Chen, R., Yu, P.S.: Privacy-preserving data publishing: a survey of recent developments. ACM Comput. Surv. (CSUR) 42(4), 14 (2010)CrossRefGoogle Scholar
  10. 10.
    Juels, A.: RFID security and privacy: a research survey. IEEE J. Sel. Areas Commun. 24(2), 381–394 (2006)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Li, N., Zhang, N., Das, S.K., Thuraisingham, B.: Privacy preservation in wireless sensor networks: a state-of-the-art survey. Ad Hoc Netw. 7(8), 1501–1514 (2009)CrossRefGoogle Scholar
  12. 12.
    Kamat, P., Xu, W., Trappe, W., Zhang, Y.: Temporal privacy in wireless sensor networks. In: 27th International Conference on Distributed Computing Systems (ICDCS 2007), pp. 23. IEEE, June 2007Google Scholar
  13. 13.
    Jian, Y., Chen, S., Zhang, Z., Zhang, L. Protecting receiver-location privacy in wireless sensor networks. In: 26th IEEE International Conference on Computer Communications (INFOCOM 2007), pp. 1955–1963. IEEE, May 2007Google Scholar
  14. 14.
    Deng, J., Han, R., Mishra, S.: Decorrelating wireless sensor network traffic to inhibit traffic analysis attacks. Pervasive Mob. Comput. 2(2), 159–186 (2006)CrossRefGoogle Scholar
  15. 15.
    He, W., Liu, X., Nguyen, H., Nahrstedt, K., Abdelzaher, T.: PDA: privacy-preserving data aggregation in wireless sensor networks. In: 26th IEEE International Conference on Computer Communications (INFOCOM 2007), pp. 2045–2053. IEEE, May 2007Google Scholar
  16. 16.
    Zhang, W., Wang, C., Feng, T.: GP2S: generic privacy-preservation solutions for approximate aggregation of sensor data (concise contribution). In: Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom 2008), pp. 179–184. IEEE, March 2008Google Scholar
  17. 17.
    Vaidya, P.G., Angadi, S.: Decoding chaotic cryptography without access to the superkey. Chaos Solitons Fractals 17(2), 379–386 (2003)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Wong, K.W., Ho, S.W., Yung, C.K.: A chaotic cryptography scheme for generating short ciphertext. Phys. Lett. A 310(1), 67–73 (2003)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Li, S., Li, Q., Li, W., Mou, X., Cai, Y.: Statistical properties of digital piecewise linear chaotic maps and their roles in cryptography and pseudo-random coding. In: IMA International Conference on Cryptography and Coding, pp. 205–221. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  20. 20.
    Pareek, N.K., Patidar, V., Sud, K.K.: Cryptography using multiple one-dimensional chaotic maps. Commun. Nonlinear Sci. Numer. Simul. 10(7), 715–723 (2005)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Pareek, N.K., Patidar, V., Sud, K.K.: Discrete chaotic cryptography using external key. Phys. Lett. A 309(1), 75–82 (2003)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Amigó, J.M., Kocarev, L., Szczepanski, J.: Theory and practice of chaotic cryptography. Phys. Lett. A 366(3), 211–216 (2007)CrossRefGoogle Scholar
  23. 23.
    Yang, T., Chua, L.O.: Chaotic digital code-division multiple access (CDMA) communication systems. Int. J. Bifurc. Chaos 7(12), 2789–2805 (1997)CrossRefGoogle Scholar
  24. 24.
    Abdullah, H.N.: Chaotic multiple access system based on orthogonal chaotic vector of Lorenz system. Al-Nahrain J. Eng. Sci. 18(2), 219–228 (2017)Google Scholar
  25. 25.
    Watanabe, H., Narumiya, Y., Hasegawa, M.: Performance evaluation of chaotic CDMA using an implemented system on software defined radio. Nonlinear Theor. Appl. IEICE 4(4), 473–481 (2013)CrossRefGoogle Scholar
  26. 26.
    Lorenz, E.N.: Deterministic nonperiodic flow. J. Atmos. Sci. 20(2), 130–141 (1963)CrossRefGoogle Scholar
  27. 27.
    Mareca, M.P., Bordel, B.: Improving the complexity of the Lorenz dynamics. Complexity (2017)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Universidad Politécnica de MadridMadridSpain

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