TI-TRNG: Technology independent true random number generator

True random number generators (TRNGs) are needed for a variety of security applications and protocols. The quality (randomness) of TRNGs depends on sensitivity to random noise, environmental conditions, and aging. Random sources of noise improve TRNG quality. In older or more mature technologies, the random sources are limited resulting in low TRNG quality. Prior work has also shown that attackers can manipulate voltage supply and temperature to bias the TRNG output. In this paper, we propose bias detection mechanisms and a technology independent TRNG (TI-TRNG) architecture. The TI-TRNG enhances power supply noise for older technologies and uses a self-calibration mechanism that reduces bias in TRNG output due to aging and attacks. Experiment results on 130nm, 90nm, and 45nm FPGAs demonstrate the quality of random sequences from the TI-TRNG across aging and different environmental conditions.

[1]  Daniel E. Holcomb,et al.  Power-Up SRAM State as an Identifying Fingerprint and Source of True Random Numbers , 2009, IEEE Transactions on Computers.

[2]  Shreyas Kumar Krishnappa,et al.  Incorporating Effects of Process , Voltage , and Temperature Variation in BTI Model for Circuit Design , 2010 .

[3]  Shekhar Y. Borkar,et al.  Designing reliable systems from unreliable components: the challenges of transistor variability and degradation , 2005, IEEE Micro.

[4]  Milos Drutarovský,et al.  True Random Number Generator Embedded in Reconfigurable Hardware , 2002, CHES.

[5]  Viktor Fischer,et al.  A Closer Look at Security in Random Number Generators Design , 2012, COSADE.

[6]  Sanu Mathew,et al.  2.4GHz 7mW all-digital PVT-variation tolerant True Random Number Generator in 45nm CMOS , 2010, 2010 Symposium on VLSI Circuits.

[7]  Yu Cao,et al.  The Impact of NBTI Effect on Combinational Circuit: Modeling, Simulation, and Analysis , 2010, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[8]  Berk Sunar,et al.  True Random Number Generators for Cryptography , 2009, Cryptographic Engineering.

[9]  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.

[10]  Berk Sunar,et al.  Improving the Robustness of Ring Oscillator TRNGs , 2010, TRETS.

[11]  David Blaauw,et al.  True Random Number Generator With a Metastability-Based Quality Control , 2007, IEEE Journal of Solid-State Circuits.

[12]  Nur A. Touba,et al.  Achieving high encoding efficiency with partial dynamic LFSR reseeding , 2004, TODE.

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

[14]  Srinivas Devadas,et al.  FPGA-Based True Random Number Generation Using Circuit Metastability with Adaptive Feedback Control , 2011, CHES.

[15]  M. D. Giles,et al.  Process Technology Variation , 2011, IEEE Transactions on Electron Devices.

[16]  Mark Mohammad Tehranipoor,et al.  Detection of trojans using a combined ring oscillator network and off-chip transient power analysis , 2013, JETC.

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

[18]  Chik How Tan,et al.  A Comparison of Post-Processing Techniques for Biased Random Number Generators , 2011, WISTP.

[19]  H. Tenhunen,et al.  Analysis of timing jitter in inverters induced by power-supply noise , 2006, International Conference on Design and Test of Integrated Systems in Nanoscale Technology, 2006. DTIS 2006..

[20]  Sanu Mathew,et al.  A 4Gbps 0.57pJ/bit Process-Voltage-Temperature Variation Tolerant All-Digital True Random Number Generator in 45nm CMOS , 2009, 2009 22nd International Conference on VLSI Design.