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

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.

[1]  Mikio Hasegawa,et al.  Performance evaluation of chaotic CDMA using an implemented system on software defined radio , 2013 .

[2]  Lei Chen,et al.  A Survey of Privacy-Preservation of Graphs and Social Networks , 2010, Managing and Mining Graph Data.

[4]  Elisa Bertino,et al.  A Survey of Quantification of Privacy Preserving Data Mining Algorithms , 2008, Privacy-Preserving Data Mining.

[5]  Wenyuan Xu,et al.  Temporal Privacy in Wireless Sensor Networks , 2007, 27th International Conference on Distributed Computing Systems (ICDCS '07).

[6]  Victor C. M. Leung,et al.  Network Slicing Based 5G and Future Mobile Networks: Mobility, Resource Management, and Challenges , 2017, IEEE Communications Magazine.

[7]  Xue Liu,et al.  PDA: Privacy-Preserving Data Aggregation in Wireless Sensor Networks , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[8]  Ljupco Kocarev,et al.  Theory and practice of chaotic cryptography , 2007 .

[9]  Mahesh K. Marina,et al.  Network Slicing in 5G: Survey and Challenges , 2017, IEEE Communications Magazine.

[10]  Borja Bordel,et al.  Cyber-physical systems: Extending pervasive sensing from control theory to the Internet of Things , 2017, Pervasive Mob. Comput..

[11]  Shivakant Mishra,et al.  Decorrelating wireless sensor network traffic to inhibit traffic analysis attacks , 2006, Pervasive Mob. Comput..

[12]  Shujun Li,et al.  Statistical Properties of Digital Piecewise Linear Chaotic Maps and Their Roles in Cryptography and Pseudo-Random Coding , 2001, IMACC.

[13]  Jian Pei,et al.  A brief survey on anonymization techniques for privacy preserving publishing of social network data , 2008, SKDD.

[14]  Vinod Patidar,et al.  Cryptography using multiple one-dimensional chaotic maps , 2005 .

[15]  Wensheng Zhang,et al.  GP^2S: Generic Privacy-Preservation Solutions for Approximate Aggregation of Sensor Data (concise contribution) , 2008, 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom).

[16]  Kwok-Wo Wong,et al.  A chaotic cryptography scheme for generating short ciphertext , 2003 .

[17]  Vinod Patidar,et al.  Discrete chaotic cryptography using external key , 2003 .

[18]  Liang Zhang,et al.  Protecting Receiver-Location Privacy in Wireless Sensor Networks , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[19]  Ari Juels,et al.  RFID security and privacy: a research survey , 2006, IEEE Journal on Selected Areas in Communications.

[20]  Sajal K. Das,et al.  Privacy preservation in wireless sensor networks: A state-of-the-art survey , 2009, Ad Hoc Networks.

[21]  Diego Sánchez de Rivera,et al.  Using 5G Technologies in the Internet of Things Handovers, Problems and Challenges , 2015, 2015 9th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.

[22]  E. Lorenz Deterministic nonperiodic flow , 1963 .

[23]  P. G. Vaidya,et al.  Decoding chaotic cryptography without access to the superkey , 2003 .

[24]  Philip S. Yu,et al.  A General Survey of Privacy-Preserving Data Mining Models and Algorithms , 2008, Privacy-Preserving Data Mining.

[25]  Philip S. Yu,et al.  Privacy-preserving data publishing: A survey of recent developments , 2010, CSUR.

[26]  Borja Bordel,et al.  Improving the Complexity of the Lorenz Dynamics , 2017, Complex..