blocks|key|847294|text|您正在做的事情听起来很像许多系统上的/dev/随机和/dev/urandom或珠三角：这些系统接受任意大的数字序列(如果有真正的硬件随机数发生器，或者来自环境噪声，如击键定时)，并将其输入CSPRNG；输出通过/dev/随机和/dev/urandom提供。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|847295|也许您可以以某种方式提取其中任何一个系统的源代码实现，并使用户级的变体，而不是从环境源获得的输入，只从您的大种子输入。|847296|使用FreeBSD和AIX实现/dev/+雅罗算法随机|unordered-list-item|847297|OpenBSD使用基于RC4的算法实现/dev/随机(这与艾萨克相同吗?)|847298|福图纳|847299|我的理解是，许多其他CSPRNG实现都有一种方式来提供更多的熵。|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki//dev/random|1|http://prngd.sourceforge.net/|2|http://en.wikipedia.org/wiki/hardware_random_number_generator|3|http://en.wikipedia.org/wiki/Yarrow_algorithm|4|http://en.wikipedia.org/wiki/ISAAC_%2528cipher%2529|5|http://en.wikipedia.org/wiki/Fortuna_%2528PRNG%2529|6|http://en.wikipedia.org/wiki/cryptographically_secure_pseudorandom_number_generator^0|I|K|0|13|3|1|1S|8|2|0|0|L|4|3|0|T|3|4|0|0|3|5|0|A|6|6^^$0|@$1|2|3|4|5|6|7|17|8|@]|9|@$A|18|B|19|1|1A]|$A|1B|B|1C|1|1D]|$A|1E|B|1F|1|1G]]|C|$]]|$1|D|3|E|5|6|7|1H|8|@]|9|@]|C|$]]|$1|F|3|G|5|H|7|1I|8|@]|9|@$A|1J|B|1K|1|1L]]|C|$]]|$1|I|3|J|5|H|7|1M|8|@]|9|@$A|1N|B|1O|1|1P]]|C|$]]|$1|K|3|L|5|H|7|1Q|8|@]|9|@$A|1R|B|1S|1|1T]]|C|$]]|$1|M|3|N|5|H|7|1U|8|@]|9|@$A|1V|B|1W|1|1X]]|C|$]]]|O|$P|$5|Q|R|S|C|$T|U]]|V|$5|Q|R|S|C|$T|W]]|X|$5|Q|R|S|C|$T|Y]]|Z|$5|Q|R|S|C|$T|10]]|11|$5|Q|R|S|C|$T|12]]|13|$5|Q|R|S|C|$T|14]]|15|$5|Q|R|S|C|$T|16]]]]

What you are doing sounds a lot like what the <a href="http://en.wikipedia.org/wiki//dev/random" rel="nofollow">/dev/random and /dev/urandom</a> or the <a href="http://prngd.sourceforge.net/" rel="nofollow">PRNGD</a> on many systems already do:
those systems take an arbitrary large sequence of numbers (from a true <a href="http://en.wikipedia.org/wiki/hardware_random_number_generator" rel="nofollow">hardware random number generator</a> if available, or else from environmental noise such as keystroke timing)
and feed it into a CSPRNG;
the output is made available via /dev/random and /dev/urandom .

Perhaps you could somehow extract the source code implementation of any one of those systems,
and make a user-level variant that, rather than getting input from environmental sources, only gets input from your large seed.

<ul>
<li>FreeBSD and AIX implement /dev/random using the <a href="http://en.wikipedia.org/wiki/Yarrow_algorithm" rel="nofollow">Yarrow algorithm</a></li>
<li>OpenBSD implements /dev/random using an algorithm based on RC4 (is this the same as <a href="http://en.wikipedia.org/wiki/ISAAC_%28cipher%29" rel="nofollow">ISAAC</a> ?)</li>
<li><a href="http://en.wikipedia.org/wiki/Fortuna_%28PRNG%29" rel="nofollow">Fortuna</a></li>
<li>My understanding is that many other <a href="http://en.wikipedia.org/wiki/cryptographically_secure_pseudorandom_number_generator" rel="nofollow">CSPRNG</a> implementations have a way to feed in more bits of entropy.</li>
</ul>

blocks|key|847250|text|Barak和Halevi提议的PRNG应该能够满足您的需要并提供足够的安全性。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|847251|他们的PRNG有一个API+next和refresh。refresh使用任意长的字符串，并使用它更新PRNG的内部状态。next返回一定数量的随机字节(如果需要更多字节，只需连续调用next+)。|847252|它们证明了它们的PRNG的一些非常好的特性(它们共同定义了PRNG的健壮性)，并给出了具体的实现。|847253|整篇论文绝对值得一读。|entityMap|0|LINK|mutability|MUTABLE|url|http://eprint.iacr.org/2005/029.pdf^0|0|J|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@$A|R|B|S|1|T]]|C|$]]|$1|D|3|E|5|6|7|U|8|@]|9|@]|C|$]]|$1|F|3|G|5|6|7|V|8|@]|9|@]|C|$]]|$1|H|3|I|5|6|7|W|8|@]|9|@]|C|$]]]|J|$K|$5|L|M|N|C|$O|P]]]]

The <a href="http://eprint.iacr.org/2005/029.pdf" rel="nofollow">PRNG proposed by Barak and Halevi</a> should be able to meet your needs and provide sufficient security. 

Their PRNG has an API of next and refresh. refresh takes an arbitrarily long string and uses it to update the internal state of the PRNG. next returns some number of random bytes (if more is needed, one can simply call next in succession).

They prove some very nice properties of their PRNG (which together define robustness of a PRNG), and give a concrete implementation.

The entire paper is definitely worth a read.

blocks|key|847332|text|别费神。以任何密码安全的CSPRNG为例。给它一个随机密钥(确保密钥长度至少为128位)。你的任务完成了。一旦密钥长度为128位左右，任何额外的长度都没有意义。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|847333|如果你有一个一千位的种子，先把它打散，把它缩小到你的CSPRNG键的正确大小--但是要确保你的种子中至少有那么多的熵。或者，更好的是，使用安全的伪随机数字生成器(如/dev/urandom或CryptGenRandom+)生成密钥。|offset|length|style|CODE|entityMap^0|0|2A|C|2N|E^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|J|8|@$D|K|E|L|F|G]|$D|M|E|N|F|G]]|9|@]|A|$]]]|H|$]]

Don't bother. Take any cryptographically secure CSPRNG. Feed it a random key (make sure the key length is at least 128 bits). You're done. Once the key length is 128 bits or so, any additional length is pointless overkill.

If you have a thousand-bit seed, just hash it first to get it down to be the right size of the key of your CSPRNG -- but make sure you have at least that much entropy in the seed. Or, better yet, simply generate your key using a secure pseudorandom number generator, like <code>/dev/urandom</code> or <code>CryptGenRandom</code>.

blocks|key|1988045|text|这一点现在可能是显而易见的(至少比凯克卡克赢得SHA-3比赛之前更明显)，但这里要求的内容听起来很像海绵函数。或海绵函数在双工模式下，如果有限状态是如此严重的关注。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|entityMap|0|LINK|mutability|MUTABLE|url|https://en.wikipedia.org/wiki/Keccak|1|https://en.wikipedia.org/wiki/Sponge_function^0|H|4|0|1E|4|1^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@$A|N|B|O|1|P]|$A|Q|B|R|1|S]]|C|$]]]|D|$E|$5|F|G|H|C|$I|J]]|K|$5|F|G|H|C|$I|L]]]]

This is probably obvious now (more obvious, at least, than before <a href="https://en.wikipedia.org/wiki/Keccak" rel="nofollow">Keccak</a> won the SHA-3 competition) but what is being asked for here sounds an awful lot like a <a href="https://en.wikipedia.org/wiki/Sponge_function" rel="nofollow">sponge function</a>. Or a sponge function in duplex mode, if the finite state is such a serious concern.

Can anyone suggest a good (CS)PRNG algorithm that takes advantage of having a very large (ideally arbitrarily large) seed? I'd like to use several kilobytes, perhaps several hundred kilobytes, of random data to generate up to a few dozen megabytes of PRNG output. The PRNG algorithms I'm aware of only take relatively small seeds: I'd like to make the most of the random input data I have.

PRNG taking advantage of very large seed

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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谁能提出一个好的(CS)PRNG算法，利用一个非常大的(理想的任意大的)种子？我想使用几千字节，也许是几百千字节的随机数据来生成几十兆字节的PRNG输出。我所知道的PRNG算法只需要相对较小的种子:我想充分利用我所拥有的随机输入数据。

问PRNG利用超大型种子
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