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from the just-pull-numbers-from-a-hat dept.
Researchers in China have developed a way to improve the reliability and security of machines that use quantum phenomena to generate random numbers. This is crucial to the development of other related technologies, such as secure quantum communication and computer simulations used in weather forecasts.
[...] "The output of [...] pseudorandom number generators is in principle predictable," said Xiongfeng Ma, an information scientist from Beijing's Tsinghua University, who was a part of the Chinese group. "They are good enough for most applications like simulations, but not for high security crypto systems."
[...] "Even if you have a very good [quantum] random number generator, there will still be some residual bias, so there needs to be a way to test and clean the data," said Juan Carlos Garcia-Escartin, a telecommunication scientist from University of Valladolid in Spain.
This need for post-measurement processing exposes the system to potential hacking. Ma and his team have developed a way to detect if a system is compromised. The basic concept is pretty simple -- they use the random source to trigger random testing of the data, kind of like pop-quizzes for a class of students.
This involves repeatedly shuffling and dividing the output numbers into four random groups, then testing them and crosschecking their results for anomalies. If the numbers are truly random and unbiased, any manipulation by an outsider would show up in these tests. Once this testing method is implemented, then even an untrusted quantum random number generator can still be used without the fear of compromising the level of randomness generated.
(Score: 2) by HiThere on Tuesday February 14 2017, @07:51PM
While it's true that there is no way to prove a sequence is random, there are ways to show it *probably* isn't. A string of 4's *could* be randomly generated, it's just unlikely, and the longer it gets, the less likely it is to be randomly generated.
< rant>
Then there's the question of bias. Even a randomly generated sequence will probably be generated with bias, in fact arbitrarily strong bias. And, in fact, EVERY random sequence is generated with bias. If it's a random sequence of digits, then each entry is constrained to fall within the integers 0-9 inclusive. This kind of constraint, as with most known constraints, doesn't limit the randomness of the sequence, but merely the randomness of the members of the sequence. To increase the randomness of the sequence you could, e.g., consider the members pairwise. etc. Other kinds of bias are harder to correct for. If the mean is closer to one of the bounds than to the center you will need extra work to generate either a uniform or normal distribution. Some distributions are multi-modal (bi-modal is the one usually considered).
Javascript is what you use to allow unknown third parties to run software you have no idea about on your computer.