True Random Number Generator using Solar Output Characteristics

Transcription

1 True Random Number Generator using Solar Output Characteristics Stephen Ritter, Tyler Pigg, Connor Brown, and Biswajit Ray Presenter: Biswajit Ray, Assistant Professor Electrical and Computer Engineering, University of Alabama in Huntsville ( 1/9/19 1

2 Internet of Things Ecosystem What is the key Challenge? Secure communication between internet and the sensor network 1/9/19 2

3 Applications of random number Encryption: Secure Communication Random sampling Scientific Simulation (Monte Carlo) Gambling and lotteries Video Game 1/9/19 3

4 State of the art: PRNG Pseudo random number generator (PRNG) Using a Mathematical Formula Deterministic Vulnerable to cyber attack A BRILLIANT SLOT MACHINE CHEAT Comments There is no perfect pseudorandom number generator We should never completely trust results from a single pseudorandom number generator Good (true) random number generators are hard to find 1/9/19 4

5 Hardware based TRNGs Read noise of Flash memory Ray et. al., TED 2018 Bit line Ring oscillator-based TRNG Wordline Ground Key points: Requires dedicated TRNG circuit (hardware change) NOT included in many existing embedded systems Not cost effective radioactive decay, single photon optical processes, Brownian motion, clock jitters, noise in electronics devices 1/9/19 5

6 Solar TRNG: Advantages Widely applicable: PV is an integral component in many IoT devices and edge computing platforms No hardware modification required Non-intrusive: The techniques do not require a reboot or system inconvenient 1/9/19 operation 6

7 Fundamentals of PV Outline Experimental details Results and discussion NIST test 1/9/19 7

8 Solar cell basics A Solar Panel Solar cell operation (4) (1) (3) (2) Equivalent Circuit Model J L J dark J total L O A D V 1) Light absorption 2) e-h pair generation 3) Thermalization (heat loss) 4) Recombination and e-h transport (carrier loss) 1/9/19 8

9 Solar cell I-V characteristics Short circuit Max power point Open circuit 1/9/19 9

10 Physics behind PV randomness 1) Variability in incident light 2) Variability in absorption 3) Variability in e-h pair recombination 4) Variability in temperature (1) (3) (2) 1/9/19 10

11 Fundamentals of PV Outline Experimental details Results and discussion NIST test 1/9/19 11

12 Experimental set-up Arduino microcontroller platform Solar module: 2.75 inch x 2.25 inch (Seed tech.) 0.5W power rating. SD card based data logger 1/9/19 12

13 Fundamentals of PV Outline Experimental details Results and discussion NIST test 1/9/19 13

14 PV output data Key points: Sun light has larger fluctuation over a period of time Randomness is also contributed from photovoltaic operation 1/9/19 14

15 Complete Algorithm Start Measure voltage across the solar cell Random bit sequence Convert the analog voltage into binary bits Perform Von-Neumann debasing Update the random number sequence with new bits No Is random bit count > requested bits? Yes 1/9/19 15 End

16 NIST Test: Randomness evaluation NIST Test P-value Result Frequency Pass Block frequency Pass Runs Pass Longest runs Pass Rank Pass FFT Pass Overlapping template Non-overlapping template Pass Universal Linear complexity Serial Pass Approximate entropy Pass Cumulative Sums Pass Random Excursion Pass Random Excursions Variant Pass 1/9/19 16

17 Throughput Analysis Sampling frequency = 10 data points/s Each data point = 10 bits (ADC) Throughput 100 bits/s 1/9/19 17

18 TRNG performance in different environment Voltage (V) Sunny day Cloudy day time (hr) Key points: TRNG output is robust across different sunlight conditions 1/9/19 18

19 PV output data: Operating cond.! " =! " = %& '! " = %(( ' Current Density, J (A/cm 2 ) Voltage (V) Key points: TRNG output is robust across different operating points 1/9/19 19

20 Conclusion (1) Solar output can be used for TRNG (2) Random numbers pass NIST tests (3) Random numbers are robust across different operating and light condition 1/9/19 20

21 Thank you Stephen Ritter, Tyler Pigg, Connor Brown 1/9/19 21