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  • 1. How to generate random numbers on RFID Tag ? Kirti Chawla [email_address]
  • 2. Basic Problem and Challenges > Basic Problem: To generate reliable and unpredictable random numbers on a RFID tag
    • > Hardware description of RFID tag:
    • Battery (Active tags) or battery-less operation (Passive Tags)
    • Paper, PET (plastic) Inlay
    • Various form factors (stamp-size to PDA-size)
    • > Challenges:
    • Limited power supply (Passive tags power off reader supplied RF signal)
    • Limited circuit space (1 RFID tag ~ 4k-8k Gates)
    • Lower limits on circuit complexity (Limits the use of stronger RNG schemes)
    • Strength of generated random numbers (True-RNG, Pseudo-RNG)
  • 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
    • > A few candidate approaches:
    • Direct Amplification
    • Oscillator Sampling
    • Discrete-time Chaos
    • Initial SRAM state
    • Physically Unclonable Functions (PUFs)
  • 4. Approach 1: Direct Amplification
    • > How it works ?
    • Use high-gain high-bandwidth OP-AMP to process the AC voltage produced by a noise (e.g. thermal or shot noise) source.
    • Noise must be sufficiently amplified to a level where it can be accurately captured in a bias-free manner.
    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. Approach 1: Direct Amplification
    • > Merits:
    • Popular technique for single-chip solution, where shielding of noise source is possible.
    • Simple concept.
    • Less power and circuit-space requirement.
    • > De-Merits:
    • 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.
    • May be affected by 1/f (pink) noise.
  • 6. Approach 2: Oscillator Sampling
    • > How it works ?
    • Use free running oscillators as a source of phase noise to generate randomness.
    • Output of a fast oscillator is sampled on the rising edge of a slower clock using D flip-flop.
    • Oscillator jitter causes randomness in exact sampled values.
    > 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. Approach 2: Oscillator Sampling
    • Merits:
    • More robust technique in the presence of deterministic noise.
    • Randomness can be artificially enhanced by careful selection of ratio of fast and slow oscillator frequencies.
    • De-Merits:
    • Research shows that, certain levels of oscillator jitter are not sufficient to produce statistical randomness.
    • Use of pseudo-random techniques to mitigate 1, can further degrade randomness of the output.
  • 8. Approach 3: Discrete-time Chaos
    • > How it works ?
    • Uses discrete-time analog signal processing techniques such as PWL system
    • Divergence of dynamic properties of the signal (or trajectory) and addition of noise generates randomness.
    > 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. Approach 3: Discrete-time Chaos
    • Merits:
    • Insensitive to the presence of deterministic noise.
    • Randomness is obtained from robust signal dynamic properties and not noise.
    De-Merits: Circuit inaccuracies that limit A/D resolution also lead to statistical non-randomness.
  • 10. Approach 4: Initial SRAM State
    • > How it works ?
    • Process variation in SRAM cell enables the noise influence to determine the outcome of the bit.
    • Well matched devices (based on doping concentration) are used as entropy source. These devices are randomly scattered over the SRAM.
    • Uses entropy extractor to for fetching entropy from randomly scattered well matched devices.
    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. Approach 4: Initial SRAM State
    • Merits:
    • Small volatile memory can be added to tag cheaply.
    • Use of Universal hash function provides statistical randomness.
    • De-Merits:
    • Gathered entropy from the scattered devices may not have statistical randomness.
    • Implementing Universal hash function can be costly on RFID tag.
  • 12. Approach 5: PUFs
    • > How it works ?
    • Maps a set of challenges to a set of responses using a intractably complex physical system.
    • Process variation causes significant delay differences between various ICs.
    • Relative delay between two paths can be measured.
    Paper: Charles W. O’Donnell, G. Edward Suh, and Srinivas Devadas, “PUF Based Random Number Generation”
  • 13. Approach 5: PUFs
    • Merits:
    • PUFs generate statistical randomness.
    • Randomness is based on easily available process variation.
    • De-Merits:
    • Corrector / decorrelator is required.
    • It is possible that in 1 run less than desired no. of bits are produced.
  • 14. References
    • Charles W. O’Donnell, G. Edward Suh, and Srinivas Devadas, “PUF Based Random Number Generation”, MIT CSAIL, Technical Memo, 2004
    • Karsten Nohl, “Implementable Privacy for RFID Systems”, Ph.D Thesis, University of Virginia, 2009
    • Damith C. Ranasinghe, “Lightweight Cryptography on Low cost RFID”, Networked RFID Systems and Lightweight Cryptography, Springer, 2007
    • 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
    • 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
    • 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
    • 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
  • 15. Questions ?