![]() ![]() (But it doesn't use any obfuscation or whitening other than the XOR shifting, so if you feed it nothing but 0s, it will output nothing but 0s and it will be obvious.) Obviously if your program turns off Timer 1 completely, this will no longer produce random numbers. ![]() The LSBs will still be noisy even if the MSBs are periodic, and XORing the two will preserve only the randomness of the LSBs. ![]() So you should be able to use all the inputs and outputs normally, and still generate random numbers. Serial correlation coefficient is -0.001267 (totally uncorrelated = 0.0). Monte Carlo value for Pi is 3.145767276 (error 0.13 percent). Would exceed this value 75.00 percent of the times.Īrithmetic mean value of data bytes is 127.4536 (127.5 = random). I've also tested without the TimerOne library ( probably_random.ino), just sampling the Arduino's constantly-cycling PWM timers instead of configuring and resetting Timer 1, and it doesn't seem to hurt the randomness: Entropy = 7.999969 bits per byte.Ĭhi square distribution for 5489591 samples is 233.95, and randomly Gallery of tests of both TrueRandom and ProbablyRandom Output scatter plotted ( plot(a, 'bo', alpha=0.1)): Generating the 13 GB required by dieharder would take about 48 years. Serial correlation coefficient is -0.007752 (totally uncorrelated = 0.0). ![]() Monte Carlo value for Pi is 3.126899835 (error 0.47 percent). Would exceed this value 15.83 percent of the times.Īrithmetic mean value of data bytes is 127.8158 (127.5 = random). Serial correlation coefficient is -0.008583 (totally uncorrelated = 0.0).įor comparison, with probably_random_with_TimerOne.ino, ent says: Entropy = 7.996943 bits per byte.Ĭhi square distribution for 65536 samples is 277.59, and randomly Monte Carlo value for Pi is 3.682216212 (error 17.21 percent). Would exceed this value 0.01 percent of the times.Īrithmetic mean value of data bytes is 93.7178 (127.5 = random). Optimum compression would reduce the sizeĬhi square distribution for 92810048 samples is 131287892.21, and randomly "TrueRandom" is not truly random: Entropy = 7.544390 bits per byte. The micro-controller and conclude that it should not be used to produceĭisclaimer: I have no idea what I'm doing.īut it measures better than TrueRandom. Ardrand: The Arduino as a Hardware Random-Number Generator "We explore various methods to extract true randomness from.True Random Number Generation on an AtmelAVR Microcontroller (8 bits per second).Entropy gathering for cryptographic applications in AVR - Qualification of WDT as entropy source (125 bits per second).I think the main flaw would be if the two oscillators become correlated to each other in certain hardware configurations or at certain points in time, which I haven't noticed, despite running it continuously for days. Though in reality, probably multiple bits have varying amounts of entropy? The raw read from the timer sampling is estimated at 4.4 bits of entropy per byte. The assumption is that at least one bit in each sample is truly random. Then the randomness is spread around to all 8 bits by reading 8 times and bit-shifting and XORing, to produce a random byte. Since the watchdog timer runs on its own RC oscillator, and Timer 1 is on the crystal oscillator, there is random variation in the value read. It uses the watchdog timer to sample (and reset) Timer 1. Post a comment if you try it on other hardware or if you find a scenario where it doesn't work. Only produces ~64 bit/s because of the minimum length of the watchdog timer. My attempt at a hardware random number generator in Arduino with no external components. ![]()
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