Hmmm... that makes it sound rather subjective. If we don't have objective measures, then who is to say that one's randomness is better or worse than another?
Understand that user data input may or may not have good entropy. But, hope that would be the only source of any output entropy, given good encryption algorithms and random keys. Was thinking in terms of how an app with access to alternate random sources, some which might be from OS or from some software, might choose one over another. Michael Hammer Principal Engineer [email protected] Mobile: +1 408-202-9291 500 Yosemite Drive Suite 120 Milpitas, CA 95035 USA -----Original Message----- From: Krisztián Pintér [mailto:[email protected]] Sent: Thursday, January 23, 2014 2:38 PM To: Michael Hammer Cc: [email protected]; [email protected] Subject: Re: [dsfjdssdfsd] Any plans for drafts or discussions on here? Michael Hammer (at Thursday, January 23, 2014, 9:49:32 PM): > This may get off-topic, but are there good software tools for testing > entropy, that could help applications determine if the underlying > system is giving them good input? disclaimer: i'm no expert, it is just what i gathered. (i'm pretty much interested in randomness.) short answer: no long answer: in some situations yes. if you are handed a bunch of data, all you can do is to try different techniques to put an upper limit on the entropy. for example you can calculate the shannon entropy assuming independent bits. then you can hypothesize some interdependence, and see if you can compress the data. you can apply different lossless compression methods. the better compression you find puts an upper limit on the entropy. but never a lower limit. you can only do better if you have an idea about the process that created the data. for example you might assume that it is mostly thermal noise. you can assume that thermal noise has some frequency distribution, or energy or whatever, etc. within this assumption, you can determine the entropy content by measurements. but at this point, you are pretty much prone to two errors: 1, what if your assumption is wrong and 2, what if your physical model overestimates the unpredictability of the given system. example for the former: the signal might be largely controllable by an external EM interference, and then you measure not noise, but attacker controlled data. example for the latter: a smartass scientist might come up with a better physical model for thermal noise. it is also important to note that entropy is observer dependent. we actually talk about the entropy as seen by the attacker. but it is not straightforward to assess what is actually visible to an attacker and what is not. observation methods improve with time.
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