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PPT

  1. 1. How to generate random numbers on RFID Tag ? Kirti Chawla [email_address]
  2. 2. Basic Problem and Challenges > Basic Problem: To generate reliable and unpredictable random numbers on a RFID tag <ul><li>> Hardware description of RFID tag: </li></ul><ul><li>Battery (Active tags) or battery-less operation (Passive Tags) </li></ul><ul><li>Paper, PET (plastic) Inlay </li></ul><ul><li>Various form factors (stamp-size to PDA-size) </li></ul><ul><li>> Challenges: </li></ul><ul><li>Limited power supply (Passive tags power off reader supplied RF signal) </li></ul><ul><li>Limited circuit space (1 RFID tag ~ 4k-8k Gates) </li></ul><ul><li>Lower limits on circuit complexity (Limits the use of stronger RNG schemes) </li></ul><ul><li>Strength of generated random numbers (True-RNG, Pseudo-RNG) </li></ul>
  3. 3. Requirements and Approaches > EPC C1 G2 Protocol for Communication at 860-960 MHz Requirement: An EPC C1 G2 compliant Tag must contain a 16-bit random or pseudo-random number generator <ul><li>> A few candidate approaches: </li></ul><ul><li>Direct Amplification </li></ul><ul><li>Oscillator Sampling </li></ul><ul><li>Discrete-time Chaos </li></ul><ul><li>Initial SRAM state </li></ul><ul><li>Physically Unclonable Functions (PUFs) </li></ul>
  4. 4. Approach 1: Direct Amplification <ul><li>> How it works ? </li></ul><ul><li>Use high-gain high-bandwidth OP-AMP to process the AC voltage produced by a noise (e.g. thermal or shot noise) source. </li></ul><ul><li>Noise must be sufficiently amplified to a level where it can be accurately captured in a bias-free manner. </li></ul>r(n) K B(n) Paper: Craig S. Petrie, and J. Alvin Connelly , “A Noise-Based IC Random Number Generator for Applications in Cryptography” > More Precisely: 1 K.r(n) < V Offset B(n) = 0 otherwise
  5. 5. Approach 1: Direct Amplification <ul><li>> Merits: </li></ul><ul><li>Popular technique for single-chip solution, where shielding of noise source is possible. </li></ul><ul><li>Simple concept. </li></ul><ul><li>Less power and circuit-space requirement. </li></ul><ul><li>> De-Merits: </li></ul><ul><li>In an integrated circuit (IC) environment, lack of appropriate shielding of noise source from power supply and substrate signals can prohibit the use of this method. </li></ul><ul><li>May be affected by 1/f (pink) noise. </li></ul>
  6. 6. Approach 2: Oscillator Sampling <ul><li>> How it works ? </li></ul><ul><li>Use free running oscillators as a source of phase noise to generate randomness. </li></ul><ul><li>Output of a fast oscillator is sampled on the rising edge of a slower clock using D flip-flop. </li></ul><ul><li>Oscillator jitter causes randomness in exact sampled values. </li></ul>> More Precisely: 1 t(n + 1) < m[C 0 + C 1 r(n) + C 2 r 2 (n)] B(n) = 0 otherwise Where, .t(n+1) = ( (t(n) + T s )MOD(C 0 + C 1 r(n) + C 2 r 2 (n)) .m = fast oscillator duty cycle. [0, 1] .C 0 , C 1 , C 2 = Model non-linear transfer function .T s = slow clock frequency .MOD = modulo operator B(n) t(n+1) r(n) Paper: Craig S. Petrie, and J. Alvin Connelly , “A Noise-Based IC Random Number Generator for Applications in Cryptography”
  7. 7. Approach 2: Oscillator Sampling <ul><li>Merits: </li></ul><ul><li>More robust technique in the presence of deterministic noise. </li></ul><ul><li>Randomness can be artificially enhanced by careful selection of ratio of fast and slow oscillator frequencies. </li></ul><ul><li>De-Merits: </li></ul><ul><li>Research shows that, certain levels of oscillator jitter are not sufficient to produce statistical randomness. </li></ul><ul><li>Use of pseudo-random techniques to mitigate 1, can further degrade randomness of the output. </li></ul>
  8. 8. Approach 3: Discrete-time Chaos <ul><li>> How it works ? </li></ul><ul><li>Uses discrete-time analog signal processing techniques such as PWL system </li></ul><ul><li>Divergence of dynamic properties of the signal (or trajectory) and addition of noise generates randomness. </li></ul>> More Precisely: 1 i(n+1) < I ref B(n) = 0 otherwise Where, .i(n+1) = A 1 [[B N (i(n) + r(n))] MOD I ref ] + A 0 .N = # of stages .B = Stage gain .I ref = Reference current .A 0 and A 1 = Sample-Hold offset and gain Paper: Craig S. Petrie, and J. Alvin Connelly , “A Noise-Based IC Random Number Generator for Applications in Cryptography”
  9. 9. Approach 3: Discrete-time Chaos <ul><li>Merits: </li></ul><ul><li>Insensitive to the presence of deterministic noise. </li></ul><ul><li>Randomness is obtained from robust signal dynamic properties and not noise. </li></ul>De-Merits: Circuit inaccuracies that limit A/D resolution also lead to statistical non-randomness.
  10. 10. Approach 4: Initial SRAM State <ul><li>> How it works ? </li></ul><ul><li>Process variation in SRAM cell enables the noise influence to determine the outcome of the bit. </li></ul><ul><li>Well matched devices (based on doping concentration) are used as entropy source. These devices are randomly scattered over the SRAM. </li></ul><ul><li>Uses entropy extractor to for fetching entropy from randomly scattered well matched devices. </li></ul>Paper: Daniel E. Holcomb, Wayne P. Burleson, and Kevin Fu, “Initial SRAM State as a Fingerprint and Source of True Random Numbers for RFID Tags”
  11. 11. Approach 4: Initial SRAM State <ul><li>Merits: </li></ul><ul><li>Small volatile memory can be added to tag cheaply. </li></ul><ul><li>Use of Universal hash function provides statistical randomness. </li></ul><ul><li>De-Merits: </li></ul><ul><li>Gathered entropy from the scattered devices may not have statistical randomness. </li></ul><ul><li>Implementing Universal hash function can be costly on RFID tag. </li></ul>
  12. 12. Approach 5: PUFs <ul><li>> How it works ? </li></ul><ul><li>Maps a set of challenges to a set of responses using a intractably complex physical system. </li></ul><ul><li>Process variation causes significant delay differences between various ICs. </li></ul><ul><li>Relative delay between two paths can be measured. </li></ul>Paper: Charles W. O’Donnell, G. Edward Suh, and Srinivas Devadas, “PUF Based Random Number Generation”
  13. 13. Approach 5: PUFs <ul><li>Merits: </li></ul><ul><li>PUFs generate statistical randomness. </li></ul><ul><li>Randomness is based on easily available process variation. </li></ul><ul><li>De-Merits: </li></ul><ul><li>Corrector / decorrelator is required. </li></ul><ul><li>It is possible that in 1 run less than desired no. of bits are produced. </li></ul>
  14. 14. References <ul><li>Charles W. O’Donnell, G. Edward Suh, and Srinivas Devadas, “PUF Based Random Number Generation”, MIT CSAIL, Technical Memo, 2004 </li></ul><ul><li>Karsten Nohl, “Implementable Privacy for RFID Systems”, Ph.D Thesis, University of Virginia, 2009 </li></ul><ul><li>Damith C. Ranasinghe, “Lightweight Cryptography on Low cost RFID”, Networked RFID Systems and Lightweight Cryptography, Springer, 2007 </li></ul><ul><li>Wenyi Che, Huan Deng, Xi Tan, and Junyu Wang, “A Random Number Generator for Application in RFID Tags”, Networked RFID Systems and Lightweight Cryptography, Springer, 2007 </li></ul><ul><li>Craig S. Petrie, M and J. Alvin Connelly, “A Noise-Based IC Random Number Generator for Applications in Cryptography”, IEEE Transactions on Circuits and Systems: Fundamental Theory, Vol. 47, No. 5, May 2000 </li></ul><ul><li>Ganesh K. Balachandran, and Raymond E. Barnett, “A 440-nA True Random Number Generator for Passive RFID Tags”, IEEE Transactions on Circuit and Systems, Vol. 55. No. 11, December 2008 </li></ul><ul><li>Daniel E. Holcomb, Wayne P. Burleson, and Kevin Fu, “Initial SRAM State as a Fingerprint and Source of True Random Numbers for RFID Tags”, RFIDSec, 2007 </li></ul>
  15. 15. Questions ?

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