Implementation and Testing of High-Speed CMOS True Random Number Generators Based on Chaotic Systems

We present the design and the validation by means of suitably improved randomness tests of two different implementations of high-performance true-random number generators which use a discrete-time chaotic circuit as their entropy source. The proposed system has been developed from a standard pipeline Analog-to-Digital converter (ADC) design, modified to operate as a set of piecewise-linear chaotic maps. The evolution of each map is observed and quantized to obtain a random bit stream. With this approach it is possible to obtain, on current CMOS technology, a data rate in the order of tens of megabit per second. Furthermore, we can also prove that the design is tamper resistant in the sense that a power analysis cannot leak information regarding the generated bits. This makes the proposed circuit perfectly suitable for embedding in cryptographic systems like smarts cards, even more so if one consider that it could be easily obtained by reconfiguring an existing pipeline ADC. The two prototypes have been designed in a 0.35-μm and 0.18-μm CMOS technology, and have a throughput of, respectively, 40 Mbit/s and 100 Mbit/s. A comparison between measured results and other high-end commercial solutions shows a comparable quality with a operating speed that is one order of magnitude faster.

[1]  Riccardo Rovatti,et al.  Second-level NIST Randomness Tests for Improving Test Reliability , 2007, 2007 IEEE International Symposium on Circuits and Systems.

[2]  R. Thewes,et al.  A low-power true random number generator using random telegraph noise of single oxide-traps , 2006, 2006 IEEE International Solid State Circuits Conference - Digest of Technical Papers.

[3]  L. Kocarev,et al.  Chaos-based random number generators-part I: analysis [cryptography] , 2001 .

[4]  Trevor Mudge,et al.  True Random Number Generator With a Metastability-Based Quality Control , 2008, IEEE J. Solid State Circuits.

[5]  Tohru Kohda Information sources using chaotic dynamics , 2001 .

[6]  S. V. Fomin,et al.  Ergodic Theory , 1982 .

[7]  Young-Sik Kim,et al.  Fast Digital TRNG Based on Metastable Ring Oscillator , 2008, CHES.

[8]  W. T. Holman,et al.  An integrated analog/digital random noise source , 1997 .

[9]  William Bialek,et al.  Entropy and Information in Neural Spike Trains , 1996, cond-mat/9603127.

[10]  Jarrod A. Roy,et al.  EPIC: Ending Piracy of Integrated Circuits , 2008, 2008 Design, Automation and Test in Europe.

[11]  Elena Trichina,et al.  Supplemental Cryptographic Hardware for Smart Cards , 2001, IEEE Micro.

[12]  J. Holleman,et al.  A 3 $\mu$W CMOS True Random Number Generator With Adaptive Floating-Gate Offset Cancellation , 2008, IEEE Journal of Solid-State Circuits.

[13]  E. G. Chester,et al.  Design of an on–chip random number generator using metastability , 2002, Proceedings of the 28th European Solid-State Circuits Conference.

[14]  Vincent Rijmen,et al.  The Design of Rijndael: AES - The Advanced Encryption Standard , 2002 .

[15]  Aleksandar M. Stankovic,et al.  Randomized modulation in power electronic converters , 2002, Proc. IEEE.

[16]  Vincent Rijmen,et al.  The Design of Rijndael , 2002, Information Security and Cryptography.

[17]  M. Delgado-Restituto,et al.  A mixed-signal integrated circuit for FM-DCSK modulation , 2004, Proceedings of the 30th European Solid-State Circuits Conference.

[18]  M. Mackey,et al.  Chaos, Fractals, and Noise: Stochastic Aspects of Dynamics , 1998 .

[19]  Ángel Rodríguez-Vázquez,et al.  Nonlinear switched-current CMOS IC for random signal generation , 1993 .

[20]  William M. Daley,et al.  Security Requirements for Cryptographic Modules , 1999 .

[21]  S. M. Ulam,et al.  On Combination of Stochastic and Deterministic Processes , 1947 .

[22]  Marco Bucci,et al.  Fully Digital Random Bit Generators for Cryptographic Applications , 2008, IEEE Transactions on Circuits and Systems I: Regular Papers.

[23]  J. Alvin Connelly,et al.  A noise-based IC random number generator for applications in cryptography , 2000 .

[24]  R. Rovatti,et al.  On the convergence to regime of ADC-based true random number generators , 2007, 2007 18th European Conference on Circuit Theory and Design.

[25]  G. Mazzini,et al.  Statistical modeling of discrete-time chaotic processes-basic finite-dimensional tools and applications , 2002, The IEEE International Symposium on Circuits and Systems, 2003. Tutorial Guide: ISCAS 2003..

[26]  R. Rovatti,et al.  A Fast Chaos-based True Random Number Generator for Cryptographic Applications , 2006, 2006 Proceedings of the 32nd European Solid-State Circuits Conference.

[27]  L. Kocarev,et al.  Chaos-based random number generators. Part II: practical realization , 2001 .

[28]  Alessandro Trifiletti,et al.  A High-Speed Oscillator-Based Truly Random Number Source for Cryptographic Applications on a Smart Card IC , 2003, IEEE Trans. Computers.

[29]  R. Ohba,et al.  Si nanodevices for random number generating circuits for cryptographic security , 2004, 2004 IEEE International Solid-State Circuits Conference (IEEE Cat. No.04CH37519).

[30]  Michael C. Mackey,et al.  Chaos, Fractals, and Noise , 1994 .

[31]  Liam Paninski,et al.  Estimating entropy on m bins given fewer than m samples , 2004, IEEE Transactions on Information Theory.

[32]  Alessandro Trifiletti,et al.  Power analysis of a chaos-based Random Number Generator for cryptographic security , 2009, 2009 IEEE International Symposium on Circuits and Systems.

[33]  Alfred Menezes,et al.  Handbook of Applied Cryptography , 2018 .

[34]  Jean-Sébastien Coron,et al.  On the Security of Random Sources , 1999, Public Key Cryptography.

[35]  Donald E. Eastlake,et al.  US Secure Hash Algorithm 1 (SHA1) , 2001, RFC.

[36]  Yun Gao,et al.  Estimating the Entropy of Binary Time Series: Methodology, Some Theory and a Simulation Study , 2008, Entropy.

[37]  Jonathon Shlens,et al.  Estimating Entropy Rates with Bayesian Confidence Intervals , 2005, Neural Computation.

[38]  R. Rovatti,et al.  Embeddable ADC-based true random number generator for cryptographic applications exploiting nonlinear signal processing and chaos , 2005 .

[39]  Ángel Rodríguez-Vázquez,et al.  Mixed-signal map-configurable integrated chaos generator for chaotic communications , 2001 .

[40]  Manuel Blum Independent unbiased coin flips from a correlated biased source—A finite state markov chain , 1986, Comb..

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

[42]  Riccardo Rovatti,et al.  A 3-GHz Serial ATA Spread-Spectrum Clock Generator Employing a Chaotic PAM Modulation , 2010, IEEE Transactions on Circuits and Systems I: Regular Papers.

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

[44]  Paul R. Gray,et al.  A 10 b, 20 Msample/s, 35 mW pipeline A/D converter , 1995, IEEE J. Solid State Circuits.

[45]  Pierre L'Ecuyer,et al.  TestU01: A C library for empirical testing of random number generators , 2006, TOMS.

[46]  Riccardo Rovatti,et al.  Experimental verification of enhanced electromagnetic compatibility in chaotic FM clock signals , 2000, 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353).

[47]  Manuel Blum,et al.  A Simple Unpredictable Pseudo-Random Number Generator , 1986, SIAM J. Comput..

[48]  Paul C. Kocher,et al.  Differential Power Analysis , 1999, CRYPTO.

[49]  Behzad Razavi,et al.  Principles of Data Conversion System Design , 1994 .