I understand how standard random number generators work. But when working with crytpography, the random numbers really have to be random.
I know there are instruments that read cosmic white noise to help generate secure hashes, but your standard PC doesn't have this.
How does a cryptographically secure random number generator get its values with no repeatable patterns?
Random number generators use mathematical formulas that transfer set of numbers to another one. If, for example, you take a constant number N and another number n_0 , and then take the value of n mod N (the modulo operator), you will get a new number n_1 , which looks as it if is unrelated to n_0 .
SecureRandom random = new SecureRandom(); byte bytes[] = new byte[20]; random. nextBytes(bytes); Callers may also invoke the generateSeed method to generate a given number of seed bytes (to seed other random number generators, for example): byte seed[] = random.
Random.org or any of their partners or your browser or the connection between you and random.org could all potentially be compromised. If someone knows that you always generate your random salts with that site, they could potentially use past generated strings to reverse engineer your crypto.
In short, a DRBG [deterministic random bit generator] is formally considered computationally secure if a computationally-limited attacker has no advantage in distinguishing it from a truly random source.
A cryptographically secure number random generator, as you might use for generating encryption keys, works by gathering entropy - that is, unpredictable input - from a source which other people can't observe.
For instance, /dev/random(4) on Linux collects information from the variation in timing of hardware interrupts from sources such as hard disks returning data, keypresses and incoming network packets. This approach is secure provided that the kernel does not overestimate how much entropy it has collected. A few years back the estimations of entropy from the various different sources were all reduced, making them far more conservative. Here's an explanation of how Linux estimates entropy.
None of the above is particularly high-throughput. /dev/random(4) probably is secure, but it maintains that security by refusing to give out data once it can't be sure that that data is securely random. If you want to, for example, generate a lot of cryptographic keys and nonces then you'll probably want to resort to hardware random number generators.
Often hardware RNGs are designed about sampling from the difference between a pair of oscillators that are running at close to the same speed, but whose rates are varied slightly according to thermal noise. If I remember rightly, the random number generator that's used for the UK's premium bond lottery, ERNIE, works this way.
Alternate schemes include sampling the noise on a CCD (see lavaRND), radioactive decay (see hotbits) or atmospheric noise (see random.org, or just plug an AM radio tuned somewhere other than a station into your sound card). Or you can directly ask the computer's user to bang on their keyboard like a deranged chimpanzee for a minute, whatever floats your boat.
As andras pointed out, I only thought to talk about some of the most common entropy gathering schemes. Thomas Pornin's answer and Johannes Rössel's answer both do good jobs of explaining how one can go about mangling gathered entropy in order to hand bits of it out again.
For cryptographic purposes, what is needed is that the stream shall be "computationally indistinguishable from uniformly random bits". "Computationally" means that it needs not be truly random, only that it appears so to anybody without access to God's own computer.
In practice, this means that the system must first gather a sequence of n truly random bits. n shall be large enough to thwart exhaustive search, i.e. it shall be infeasible to try all 2^n combinations of n bits. This is achieved, with regards to today's technology, as long as n is greater than 90-or-so, but cryptographers just love powers of two, so it is customary to use n = 128.
These n random bits are obtained by gathering "physical events" which should be unpredictable, as far as physics are concerned. Usually, timing is used: the CPU has a cycle counter which is updated several billions times per second, and some events occur with an inevitable amount of jitter (incoming network packets, mouse movements, key strokes...). The system encodes these events and then "compresses" them by applying a cryptographically secure hash function such as SHA-256 (output is then truncated to yield our n bits). What matters here is that the encoding of the physical events has enough entropy: roughly speaking, that the said events could have collectively assumed at least 2^n combinations. The hash function, by its definition, should make a good job at concentrating that entropy into a n-bit string.
Once we have n bits, we use a PRNG (Pseudo-Random Number Generator) to crank out as many bits as necessary. A PRNG is said to be cryptographically secure if, assuming that it operates over a wide enough unknown n-bit key, its output is computationally indistinguishable from uniformly random bits. In the 90's, a popular choice was RC4, which is very simple to implement, and quite fast. However, it turned out to have measurable biases, i.e. it was not as indistinguishable as was initially wished for. The eSTREAM Project consisted in gathering newer designs for PRNG (actually stream ciphers, because most stream ciphers consist in a PRNG, which output is XORed with the data to encrypt), documenting them, and promoting analysis by cryptographers. The eSTREAM Portfolio contains seven PRNG designs which were deemed secure enough (i.e. they resisted analysis and cryptographers tend to have a good understanding of why they resisted). Among them, four are "optimized for software". The good news is that while these new PRNG seem to be much more secure than RC4, they are also noticeably faster (we are talking about hundreds of megabytes per second, here). Three of them are "free for any use" and source code is provided.
From a design point of view, PRNG reuse much of the elements of block ciphers. The same concepts of avalanche and diffusion of bits into a wide internal state are used. Alternatively, a decent PRNG can be built from a block cipher: simply use the n-bit sequence as key into a block cipher, and encrypt successive values of a counter (expressed as a m-bit sequence, if the block cipher uses m-bit blocks). This produces a pseudo-random stream of bits which is computationally indistinguishable from random, as long as the block cipher is secure, and the produced stream is no longer than m*2^(m/2) bits (for m = 128, this means about 300 billions of gigabytes, so that's big enough for most purposes). That kind of usage is known as counter mode (CTR).
Usually, a block cipher in CTR mode is not as fast as a dedicated stream cipher (the point of the stream cipher is that, by forfeiting the flexibility of a block cipher, better performance is expected). However, if you happen to have one of the most recent CPU from Intel with the AES-NI instructions (which are basically an AES implementation in hardware, integrated in the CPU), then AES with CTR mode will yield unbeatable speed (several gigabytes per second).
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