Your first two categories are talking about the distribution of entropy--we assume some unpredictability exists, and we want to quantify it in terms of bits of entropy per bit of output. That's a useful distinction to make, and as you said, if you can get even a little entropy per bit and know how much you're getting, you can get something very close to ideal random bits out.
Your second two categories are talking about different kinds of sources--completely deterministic, or things that can have randomness but don't always. That leaves out sources that always have a particular amount of entropy (or at least are always expected to!). I'd say even the "squish" category can be useful in two ways: a. If you have sensible mechanisms for collecting entropy, they can't hurt and sometimes help. For example, if you sample an external clock, most of the time, the answer may be deterministic, but once in awhile, you may get some actual entropy, in the sense that the clock drift is sufficient that the sampled value could have one of two values, and an attacker can't know which. b. If you sample enough squishes, you may accumulate a lot of entropy. Some ring oscillator designs are built like this, hoping to occasionally sample the transition in value on one of the oscillators. The idea is that the rest of the behavior of the oscillators might possibly be predicted by an attacker, but what value gets read when you sample a value that's transitioning between a 0 and a 1 is really random, changed by thermal noise. I think the big problem with (b) is in quantifying the entropy you get. I also think that (b) describes a lot of what commonly gets collected by the OS and put into the entropy pool. --John _______________________________________________ The cryptography mailing list email@example.com http://www.metzdowd.com/mailman/listinfo/cryptography