 Cryptographically secure pseudorandom number generator

A cryptographically secure pseudorandom number generator (CSPRNG) is a pseudorandom number generator (PRNG) with properties that make it suitable for use in cryptography.
Many aspects of cryptography require random numbers, for example:
 Key generation
 Nonces
 Onetime pads
 Salts in certain signature schemes, including ECDSA, RSASSAPSS.
The "quality" of the randomness required for these applications varies. For example creating a nonce in some protocols needs only uniqueness. On the other hand, generation of a master key requires a higher quality, such as more entropy. And in the case of onetime pads, the informationtheoretic guarantee of perfect secrecy only holds if the key material is obtained from a true random source with high entropy.
Ideally, the generation of random numbers in CSPRNGs uses entropy obtained from a high quality source, which might be a hardware random number generator or perhaps unpredictable system processes — though unexpected correlations have been found in several such ostensibly independent processes. From an information theoretic point of view, the amount of randomness, the entropy that can be generated is equal to the entropy provided by the system. But sometimes, in practical situations, more random numbers are needed than there is entropy available. Also the processes to extract randomness from a running system are slow in actual practice. In such instances, a CSPRNG can sometimes be used. A CSPRNG can "stretch" the available entropy over more bits.
When all the entropy we have is available before algorithm execution begins, we really have a stream cipher. However some crypto system designs allow for the addition of entropy during execution, in which case it is not a stream cipher equivalent and cannot be used as one. Stream cipher and CSPRNG design is thus closely related.
Contents
Requirements
The requirements of an ordinary PRNG are also satisfied by a cryptographically secure PRNG, but the reverse is not true. CSPRNG requirements fall into two groups: first, that they pass statistical randomness tests; and secondly, that they hold up well under serious attack, even when part of their initial or running state becomes available to an attacker.
 Every CSPRNG should satisfy the "nextbit test". The nextbit test is as follows: Given the first k bits of a random sequence, there is no polynomialtime algorithm that can predict the (k+1)th bit with probability of success better than 50%. Andrew Yao proved in 1982 that a generator passing the nextbit test will pass all other polynomialtime statistical tests for randomness.
 Every CSPRNG should withstand "state compromise extensions". In the event that part or all of its state has been revealed (or guessed correctly), it should be impossible to reconstruct the stream of random numbers prior to the revelation. Additionally, if there is an entropy input while running, it should be infeasible to use knowledge of the input's state to predict future conditions of the CSPRNG state.

 Example: If the CSPRNG under consideration produces output by computing bits of π in sequence, starting from some unknown point in the binary expansion, it may well satisfy the nextbit test and thus be statistically random, as π appears to be a random sequence. (This would be guaranteed if π is a normal number, for example.) However, this algorithm is not cryptographically secure; an attacker who determines which bit of pi (i.e. the state of the algorithm) is currently in use will be able to calculate all preceding bits as well.
Most PRNGs are not suitable for use as CSPRNGs and will fail on both counts. First, while most PRNGs outputs appear random to assorted statistical tests, they do not resist determined reverse engineering. Specialized statistical tests may be found specially tuned to such a PRNG that shows the random numbers not to be truly random. Second, for most PRNGs, when their state has been revealed, all past random numbers can be retrodicted, allowing an attacker to read all past messages, as well as future ones.
CSPRNGs are designed explicitly to resist this type of cryptanalysis.
Some background
Santha and Vazirani proved that several bit streams with weak randomness can be combined to produce a higherquality quasirandom bit stream.^{[1]} Even earlier, John von Neumann proved that a simple algorithm can remove a considerable amount of the bias in any bit stream^{[2]} which should be applied to each bit stream before using any variation of the SanthaVazirani design. The field is termed entropy extraction and is the subject of active research (e.g., N Nisan, S Safra, R Shaltiel, A TaShma, C Umans, D Zuckerman).
Designs
In the discussion below, CSPRNG designs are divided into three classes: 1) those based on cryptographic primitives such as ciphers and cryptographic hashes, 2) those based upon mathematical problems thought to be hard, and 3) specialpurpose designs. The last often introduce additional entropy when available and, strictly speaking, are not "pure" pseudorandom number generators, as their output is not completely determined by their initial state. This addition can prevent attacks even if the initial state is compromised.
Designs based on cryptographic primitives
 A secure block cipher can be converted into a CSPRNG by running it in counter mode. This is done by choosing a random key and encrypting a zero, then encrypting a 1, then encrypting a 2, etc. The counter can also be started at an arbitrary number other than zero. Obviously, the period will be 2^{n} for an nbit block cipher; equally obviously, the initial values (i.e., key and "plaintext") must not become known to an attacker,however good this CSPRNG construction might be. Otherwise, all security will be lost.
 A cryptographically secure hash of a counter might also act as a good CSPRNG in some cases. In this case, it is also necessary that the initial value of this counter is random and secret. However, there has been little study of these algorithms for use in this manner, and at least some authors warn against this use.^{[3]}
 Most stream ciphers work by generating a pseudorandom stream of bits that are combined (almost always XORed) with the plaintext; running the cipher on a counter will return a new pseudorandom stream, possibly with a longer period. The cipher is only secure if the original stream is a good CSPRNG (this is not always the case: see RC4 cipher). Again, the initial state must be kept secret.
Number theoretic designs
 The Blum Blum Shub algorithm has a security proof, based on the difficulty of the Quadratic residuosity problem. Since the only known way to solve that problem is to factor the modulus, it is generally regarded that the difficulty of integer factorization provides a conditional security proof for the Blum Blum Shub algorithm. However the algorithm is very inefficient and therefore impractical unless really extreme security is needed.
 The BlumMicali algorithm has an unconditional security proof based on the difficulty of the discrete logarithm problem but is also very inefficient.
Special designs
There are a number of practical PRNGs that have been designed to be cryptographically secure, including
 the Yarrow algorithm which attempts to evaluate the entropic quality of its inputs. Yarrow is used in FreeBSD, OpenBSD and Mac OS X (also as /dev/random)
 the Fortuna algorithm, the successor to Yarrow, which does not attempt to evaluate the entropic quality of its inputs.
 the function CryptGenRandom provided in Microsoft's Cryptographic Application Programming Interface
 ISAAC based on a variant of the RC4 cipher
 ANSI X9.17 standard (Financial Institution Key Management (wholesale)), which has been adopted as a FIPS standard as well. It takes as input a TDEA (keying option 2) key bundle k and (the initial value of) a 64 bit random seed s.^{[4]} Each time a random number is required it:
 Obtains the current date/time D to the maximum resolution possible.
 Computes a temporary value t = TDEA_{k}(D)
 Computes the random value x = TDEA_{k}(s ⊕ t), where ⊕ denotes bitwise exclusive or.
 Updates the seed s = TDEA_{k}(x ⊕ t)
 Obviously, the technique is easily generalized to any block cipher; AES has been suggested (Young and Yung, op cit, sect 3.5.1).
Standards
Several CSPRNGs have been standardized. For example,
 FIPS 1862
 NIST SP 80090: Hash_DRBG, HMAC_DRBG, CTR_DRBG and Dual EC DRBG.
 ANSI X9.171985 Appendix C
 ANSI X9.311998 Appendix A.2.4
 ANSI X9.621998 Annex A.4, obsoleted by ANSI X9.622005, Annex D (HMAC_DRBG)
A good reference is maintained by NIST.
There are also standards for statistical testing of new CSPRNG designs:
 A Statistical Test Suite for Random and Pseudorandom Number Generators, NIST Special Publication 80022.
References
 ^ Miklos Santha, Umesh V. Vazirani (19841024). "Generating quasirandom sequences from slightlyrandom sources". Proceedings of the 25th IEEE Symposium on Foundations of Computer Science. University of California. pp. 434–440. ISBN 081860591X. http://www.cs.berkeley.edu/~vazirani/pubs/quasi.pdf. Retrieved 20061129.
 ^ John von Neumann (19630301). "Various techniques for use in connection with random digits". The Collected Works of John von Neumann. Pergamon Press. pp. 768–770. ISBN 0080095666.
 ^ Adam Young, Moti Yung (20040201). Malicious Cryptography: Exposing Cryptovirology. sect 3.2: John Wiley & Sons. pp. 416. ISBN 9780764549755. http://eu.wiley.com/WileyCDA/WileyTitle/productCd0764549758.html.
 ^ Handbook of Applied Cryptography, Alfred Menezes, Paul van Oorschot, and Scott Vanstone, CRC Press, 1996, Chapter 5 Pseudorandom Bits and Sequences (PDF)
External links
 RFC 4086, Randomness Requirements for Security
 Java "entropy pool" for cryptographicallysecure unpredictable random numbers.
 Java standard class providing a cryptographically strong pseudorandom number generator (PRNG).
 Cryptographically Secure Random number on Windows without using CryptoAPI
 Conjectured Security of the ANSINIST Elliptic Curve RNG, Daniel R. L. Brown, IACR ePrint 2006/117.
 A Security Analysis of the NIST SP 80090 Elliptic Curve Random Number Generator, Daniel R. L. Brown and Kristian Gjosteen, IACR ePrint 2007/048. To appear in CRYPTO 2007.
 Cryptanalysis of the Dual Elliptic Curve Pseudorandom Generator, Berry Schoenmakers and Andrey Sidorenko, IACR ePrint 2006/190.
 Efficient Pseudorandom Generators Based on the DDH Assumption, Reza Rezaeian Farashahi and Berry Schoenmakers and Andrey Sidorenko, IACR ePrint 2006/321.
 Analysis of the Linux Random Number Generator, Zvi Gutterman and Benny Pinkas and Tzachy Reinman.
 An implementation of a cryptographically safe shrinking pseudorandom number generator.
Cryptography Symmetrickey algorithm · Block cipher · Stream cipher · Publickey cryptography · Cryptographic hash function · Message authentication code · Random numbers · SteganographyCategories: Cryptographic algorithms
 Pseudorandom number generators
 Cryptographically secure pseudorandom number generators
 Cryptographic primitives
Wikimedia Foundation. 2010.
Look at other dictionaries:
Pseudorandom number generator — A pseudorandom number generator (PRNG), also known as a deterministic random bit generator (DRBG),[1] is an algorithm for generating a sequence of numbers that approximates the properties of random numbers. The sequence is not truly random in… … Wikipedia
Hardware random number generator — This SSL Accelerator computer card uses a hardware random number generator to generate cryptographic keys to encrypt data sent over computer networks. In computing, a hardware random number generator is an apparatus that generates random numbers… … Wikipedia
Random number generator attack — The security of cryptographic systems depends on some secret data that is known to authorized persons but unknown and unpredictable to others. To achieve this unpredictability, some randomization is typically employed. Modern cryptographic… … Wikipedia
Random number generation — A random number generator (often abbreviated as RNG) is a computational or physical device designed to generate a sequence of numbers or symbols that lack any pattern, i.e. appear random. Computer based systems for random number generation are… … Wikipedia
Linear congruential generator — A linear congruential generator (LCG) represent one of the oldest and best known pseudorandom number generator algorithms. [ [http://demonstrations.wolfram.com/LinearCongruentialGenerators/ Linear Congruential Generators] by Joe Bolte, The… … Wikipedia
List of number theory topics — This is a list of number theory topics, by Wikipedia page. See also List of recreational number theory topics Topics in cryptography Contents 1 Factors 2 Fractions 3 Modular arithmetic … Wikipedia
CryptGenRandom — is a cryptographically secure pseudorandom number generator function that is included in Microsoft s Cryptographic Application Programming Interface. In Win32 programs, Microsoft recommends its use anywhere random number generation is needed. A… … Wikipedia
/dev/random — In Unix like operating systems, /dev/random is a special file that serves as a random number generator or as a pseudorandom number generator. It allows access to environmental noise collected from device drivers and other sources.[citation… … Wikipedia
Onetime pad — Excerpt from a one time pad In cryptography, the one time pad (OTP) is a type of encryption, which has been proven to be impossible to crack if used correctly. Each bit or character from the plaintext is encrypted by a modular addition with a bit … Wikipedia
Generador de números pseudoaleatorios criptográficamente seguro — Un Generador de números pseudo aleatorios criptográficamente seguro (CSPRNG, del inglés «Cryptographically Secure PseudoRandom Number Generator») es un Generador de números pseudo aleatorios (PRNG) con características que lo hacen adecuado para… … Wikipedia Español