A novel S-box-based postprocessing method for true random number generation

The quality of randomness in numbers generated by true random number generators (TRNGs) depends on the source of entropy. However, in TRNGs, sources of entropy are affected by environmental changes and this creates a correlation between the generated bit sequences. Postprocessing is required to remove the problem created by this correlation in TRNGs. In this study, an S-box-based postprocessing structure is proposed as an alternative to the postprocessing structures seen in the published literature. A ring oscillator (RO)-based TRNG is used to demonstrate the use of an S-box for postprocessing and the removal of correlations between number sequences. The statistical properties of the numbers generated through postprocessing are obtained according to the entropy, autocorrelation, statistical complexity measure, and the NIST 800.22 test suite. According to the results, the postprocessing successfully removed the correlation. Moreover, the data rate of the bit sequence generated by the proposed postprocessing is reduced to 2/3 of its original value at the output.

[1]  Wayne P. Burleson,et al.  Entropy extraction in metastability-based TRNG , 2010, 2010 IEEE International Symposium on Hardware-Oriented Security and Trust (HOST).

[2]  Tang Ming . Wei Lian. Si Tuo Lin Si,et al.  Cryptography and Network Security - Principles and Practice , 2015 .

[3]  Mladen D. Veinovic,et al.  Advancement of True Random Number Generators Based on Sound Cards Through Utilization of a New Post-processing Method , 2016, Wirel. Pers. Commun..

[4]  Kris Gaj,et al.  An embedded true random number generator for FPGAs , 2004, FPGA '04.

[5]  Ihsan Pehlivan,et al.  A novel high speed Artificial Neural Network–based chaotic True Random Number Generator on Field Programmable Gate Array , 2018, Int. J. Circuit Theory Appl..

[6]  Elaine B. Barker,et al.  A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications , 2000 .

[7]  Berk Sunar,et al.  A Provably Secure True Random Number Generator with Built-In Tolerance to Active Attacks , 2007, IEEE Transactions on Computers.

[8]  Ingrid Verbauwhede,et al.  FPGA Vendor Agnostic True Random Number Generator , 2006, 2006 International Conference on Field Programmable Logic and Applications.

[9]  Christof Paar,et al.  Understanding Cryptography: A Textbook for Students and Practitioners , 2009 .

[10]  Ronen Shaltiel,et al.  True Random Number Generators Secure in a Changing Environment , 2003, CHES.

[11]  Taner Tuncer.  Implementation of Duplicate TRNG on FPGA by Using Two Different Randomness Source , 2015 .

[12]  J.D. Golic,et al.  New Methods for Digital Generation and Postprocessing of Random Data , 2006, IEEE Transactions on Computers.

[13]  Mustafa Türk,et al.  Hybrid pseudo-random number generator for cryptographic systems , 2015 .

[14]  Markus Dichtl.  Bad and Good Ways of Post-processing Biased Physical Random Numbers , 2007, FSE.

[15]  İsmail Koyuncu,et al.  The Chaos-Based Dual Entropy Core TRNG On FPGA: VHDL CODES of Chaotic Systems , 2019 .

[16]  Çetin Kaya Koç,et al.  About Cryptographic Engineering , 2008, Cryptographic Engineering.

[17]  A. Özer,et al.  A novel chaos-based post-processing for TRNG , 2015, Nonlinear Dynamics.

[19]  Chao Wan,et al.  High-throughput Von Neumann post-processing for random number generator , 2018, 2018 International Symposium on VLSI Design, Automation and Test (VLSI-DAT).

[20]  Chik How Tan,et al.  Analysis and Enhancement of Random Number Generator in FPGA Based on Oscillator Rings , 2008, 2008 International Conference on Reconfigurable Computing and FPGAs.

[21]  Ismail Koyuncu,et al.  The design and realization of a new high speed FPGA-based chaotic true random number generator , 2017, Comput. Electr. Eng..

[22]  Erdinç Avaroğlu.  Pseudorandom number generator based on Arnold cat map and statistical analysis , 2017 .

[23]  Stafford E. Tavares,et al.  On the Design of S-Boxes , 1985, CRYPTO.