## Truly random?!

**H**aving purchased the September edition of ** La Recherche** because of its (disappointing!) coverage on black matter, I came by a short coverage on an Intel circuit producing “truly random” numbers… I already discussed this issue in an earlier post, namely that there is no reason physical generators are “more” random than congruential pseudo-random generators, but this short paper repeats the same misunderstanding on the role of “random” generators. The paper mentions dangers of pseudo-random generators for cryptography (but this is only when you know the deterministic function and the sequence of seeds used so far), while it misses the essential aspect of valid generators, namely that their distribution is exactly known (e.g., uniform) and, in the case of parallel generations, which seems to be the case for this circuit, that the generators are completely independent.

**mentions that the entropy of the generator is really high, but this is more worrying than reassuring, as the Intel engineers do not have a more elaborate way to prove uniformity than a Monte Carlo experiment…**

*La Recherche***T**here is actually a deeper entry found on ** Technology Review**. (Which may have been the source for the paper in the technology tribune of

**.) The article mentions that the generator satisfied all benchmarks of “randomness” maintained by NIST. Those statistical tests sound much more reassuring than the entropy check mentioned by**

*La Recherche***, as they essentially reproduce Marsaglia’s DieHard benchmark… I remain rather skeptical about physical devices, as compared with mathematical functions, because of (a) non-reproducibility which is a negative feature despite what the paper says and of (b) instability of the device, which means that proven uniformity at time**

*La Recherche**t*does not induce uniformity at time

*t+1*. Nonetheless, if the gains in execution are gigantic, it may be worth the approximation for most applications. But please stop using “true” in conjunction with randomness!!!

February 6, 2011 at 12:12 am

[…] (or even worse) fatal attraction holds for randomness! (Although I had already posted on the “truly random” generators…) Having access only to one chapter, I read with a sense of growing puzzlement through […]

December 10, 2010 at 6:15 am

[…] very efficient: on average, 7 binary random numbers are detected per hour… A far cry from the “truly random” generator produced by […]

September 23, 2010 at 8:45 am

[…] Chapter 2 (Random generation) will update the section on uniform generators and include a section on ABC. (I will also include a note on the irrelevance of hardware random generators.) […]

September 14, 2010 at 11:52 am

Just noticed an entry on a not-quite-random generator on Biostatmatt!

September 7, 2010 at 10:20 pm

That’s an interesting thought about hardware wearing out. That is, by some physical breakdown, the ‘true’ RNG simply stops producing a truly independent and uniformly random sequence. It’s bound to happen, but I wonder how. Will the sequence begin to exhibit autocorrelation, or just start spitting out 42s? I suppose a pseudo-RNG is not subject to this problem.