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Suppose I have a list of randomly generated uint_8's, uniform over the whole range of uint_8. I write them into a file as raw bytes, no encoding.

Then I reopen the file and interpret the file as packed uint_16's (by pairing up adjacent bytes).

  1. Is my reinterpreted stream of uint_16's a uniformly random distribution in the whole range of uint_16?

  2. Now I reinterpret the bytes as packed int_32's. Is my reinterpreted stream a uniformly random distribution of int_32's, in the whole range of int_32?

Note: uintN_t is the type of N-bit unsigned integers, i.e. integers in the range $[0,2^N-1]$. intN_t is the corresponding signed type, i.e. integers in the range $[-2^{N-1},2^{N-1}-1]$.

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A small complement: if int32_t isn't the usual 2's complement with no trap value, then the signed values may not be uniformly distributed. For example, with sign-magnitude and no trap value, +0 == -0 is overrepresented. – Gilles Sep 11 '13 at 15:58
You need not only "uniformly distributed", but also "independent". – Paŭlo Ebermann Sep 11 '13 at 17:46
up vote 6 down vote accepted

Well, it rather depends on what you mean by "uniformly distributed". If you mean that, over all possible lists, each byte position was from a uniform distribution, that doesn't actually tell you that.

Here's the problem: all "uniformly distributed" in this case means is that, in a sufficiently large sample, each value occurs approximately equally often (in the uint_8 case, about 1/256 of the time).

However, it says nothing about correlations, that is, which values tend to immediately follow other values. It is easy to design a 'random string' that is perfectly uniform, however the adjacent bytes are strongly biased; this means that, when you combine adjacent bytes, each uint_16 will not be equally represented. For example, consider a string where, after a byte value $X$, the next position is also the value $X$ with probability 1/2+1/512, and a value $Y \neq X$ with probability 1/512. This sequence is equidistributed; however if you combine uint_8 values, the resulting sequence is strongly biased.

However, if by "uniformly distributed", you mean that the $N$-byte sequence as a whole was selected uniformly from all possible $256^N$ possible sequences, then yes, uniformness is preserved by combining adjacent elements.

Now, getting down to practice: if the list was generated by a cryptographically secure random number generator (CSRNG), then we can assume that combining adjacent elements is harmless. That's because we assume that the output of a CSRNG cannot be efficiently distinguished from random by any method; if the attacker could distinguish it by combining adjacent bytes, that would mean that we really didn't have a CSRNG in the first place.

On the other hand, if the list was generated by a statistical random number generator (that is, a generator that was designed to generate output that "looks" random, that is, passes some standard statistical tests), then it's a tad trickier. Real life generators can have problems when you combine outputs at the "wrong" size; however it is unlikely that one used in practice would have a problem with combining only 2 or 4 elements. So, in this case, you're probably safe (although you might want to check with the generator to be sure)

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