Returns a KeyGenerator object that generates secret keys for the specified algorithm.
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Conversations about cryptography are common place in the cyber-security world. One can find security professionals discussing everything from PKI to issues with RSA. But while we are discussing issues with algorithms, implementation of cryptographic protocols, authentication algorithms, and other such topics, we often lose sight of a fundamental part of the entire process – key generation.
Whether your preferred symmetric cypher is the U.S. Government endorsed AES, the open source BlowFish from Bruce Schneier, or the Russian GOST cipher, they all have one thing in common: they need a key. Certainly, security professionals are aware that these algorithms utilize an encryption key, but there is often too little discussion of how that key gets generated.
Key Generator
Ideally, the output of any encryption algorithm, will appear very nearly to be random.This also requires that the key utilized in that algorithm also be nearly random. This brings us to pseudo random number generators.They are called ‘pseudo’ because the output is not truly completely random.
Pseudo-random number generators (PRNGs) are algorithms that can create long runs of numbers with good random properties but eventually the sequence repeats. Thus, the term ‘pseudo’ random number generators.
- Sep 13, 2013 von Neumann used a simple pseudo-random number generator called the middle square that works as follows. You start with some number (called a seed) and square it. You take the four middle digits as your random number and square them to get the next random number, and so on.
- Jun 03, 2012 For the Love of Physics - Walter Lewin - May 16, 2011 - Duration: 1:01:26. Lectures by Walter Lewin. They will make you ♥ Physics. Recommended for you.
- What’s the best way of generating a unique key, that can’t be guessed easily? I would like to create a unique key for both account activation and referral purposes, that includes a checksum to help prevent users from easily guessing other users activation or referral keys. Also, in PHP is it possible to create you own session key?
The algorithms essentially generate numbers that, while not being truly random, are random enough for cryptographic applications. In addition to being used for generating symmetric cipher keys, PRNG’s are also used to generate Initialization Vectors for use with stream ciphers.
So the question becomes, is the PRNG you are using to generate your keys and your initialization vectors, random enough? There are some well-established PRNG algorithms such as Yarrow; Blum, Shub; and some of the Lagged Fibonacci Generators.But it is not sufficient to memorize a few algorithms that are currently considered good choices.A security professional should know what makes a good PRNG.There are four properties any good PRNG should have:
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- Uncorrelated Sequences – No sequence of any given link should be correlated to any other sequence of the algorithms output. One cannot take a given stretch of numbers (say 16 bits) and use that to predict subsequent bits.
- Long Period – Ideally the series of digits (usually bits) should never have any repeating pattern.However, the reality is that there will eventually be some repetition. The distance (in digits or bits) between repetition’s is the algorithm output period. The longer the period the better the more effective the PRNG (James, 1990; Ripley, 1990).
- Uniformity– In cryptographic applications, the output of a PRNG will most likely be represented in binary format. There should be an equal number of 1’s and 0’s (Ripley, 1990), though not distributed in any discernable pattern.The sequence of random numbers should be uniform, and unbiased. If you have significantly more (or significantly less) 1’s than 0’s then the output is biased (Soto, 2012).
- Computational Indistinguishability– Any subsection of numbers taken from the output of a given PRNG should not be distinguishable from any other subset of numbers in polynomial time by any efficient procedure. The two sequences are indistinguishable. That does not, however mean they are identical. It means there is no efficient way to determine specific differences.
The German Federal Office for Information Security (BSI) has established four criteria for quality of random number generators:
- K1A sequence of random numbers with a low probability of containing identical consecutive elements.
- K2A sequence of numbers which is indistinguishable from 'true random' numbers according to specified statistical tests.
- K3It should be impossible for any attacker to calculate, or otherwise guess, from any given sub-sequence, any previous or future values in the sequence.
- K4It should be impossible for an attacker to calculate, or guess from an inner state of the generator, any previous numbers in the sequence or any previous inner generator states.
To be suitable for cryptography any PRNG should meet K3 and K4 standards.That means that any given sequence from the output of a PRNG cannot be used to predict any future, or even any previous outputs from that PRNG. Furthermore, that even if one has access to the internal state of a PRNG, for example by examining the code at a particular stop point in the execution, that the data of the current state of the PRNG also cannot be used to product any previous or subsequent numbers in the sequence.
The United States NIST has a document describing in detail how a PRNG should be tested to ensure that it is suitable for cryptographic purposes.This 131 page document is fortunately, not a difficult read. It outlines very specific tests that can be conducted on the output of any PRNG to see if that output is ‘random enough’ for cryptographic purposes.
The good news is not that you need to become a mathematician capable of creating your own PRNG algorithm.However, when selecting cryptographic software, modules, and hardware, you need to be able to ask intelligent questions of the vendor, so that you can determine if they are using a good PRNG.A poorly chosen PRNG will weaken the security of the rest of your cryptographic solutions.
References and Further Reading
- Selected articles on Key Management (2012-16), by Ashiq JA, Chuck Easttom, Dawn M. Turner, Guillaume Forget, James H. Reinholm, Matt Landrock, Peter Landrock,Steve Marshall, Torben Pedersen and more
- Lagged Fibonacci Random Number Generators for Distributed Memory (1997), by S. Aluru. In Journal of Parallel And Distributed Computing 45, 1–12, New York City, NY, McGraw-Hill Publishing
Modern Cryptography: Applied Mathematics for Encryption and Information Security (2015), by Chuck Easttom
Yarrow-160: Notes on the design and analysis of the yarrow cryptographic pseudorandom number generator (1999, August), by j. Kelsey, B. Schneier, & n. Ferguson. In International Workshop on Selected Areas in Cryptography (pp. 13-33). Springer Berlin Heidelberg.
A review of pseudorandom number generators(1990), by F. James. In Computer Physics Communications, 60(3), 329-344.
A Statistical Test Suite for Random And Pseudorandom Number Generators For Cryptographic Applications(2001) by National Institute of Standards and Technology NIST (2001)
Random number generators: good ones are hard to find (1988) by S.K. Park, & K.W. Miller. In Communications of the ACM, 31(10), 1192-1201
Thoughts on pseudorandom number generators (1990), by B.D. Ripley. In .Journal of Computational and Applied Mathematics, 31(1), 153-163.
A Simple Unpredictable Pseudo-Random Number Generator (1986), by L. Blum, M. Blum, M. Shub. In Society for Industrial and Applied Mathematics, 15(2).
Concrete Security of the Blum-Blum-Shub Pseudorandom Generator(2005), A. Sidorenko, B. Schoenmakers . Cryptography and Coding, 3796
A selection of books by Chuck Easttom
Photo Binary Code courtesy of Christiaan Colen (CC BY-SA 2.0)
-->Definition
Represents the abstract class from which all implementations of cryptographic random number generators derive.
- Derived
- Attributes
- Implements
Remarks
Cryptographic random number generators create cryptographically strong random values.
To create a random number generator, call the Create() method. This is preferred over calling the constructor of the derived class RNGCryptoServiceProvider, which is not available on all platforms.
Constructors
| RandomNumberGenerator() | Initializes a new instance of RandomNumberGenerator. |
Methods
Random Key Generator For Crypto 2017
| Create() | Creates an instance of the default implementation of a cryptographic random number generator that can be used to generate random data. |
| Create(String) | Creates an instance of the specified implementation of a cryptographic random number generator. |
| Dispose() | When overridden in a derived class, releases all resources used by the current instance of the RandomNumberGenerator class. |
| Dispose(Boolean) | When overridden in a derived class, releases the unmanaged resources used by the RandomNumberGenerator and optionally releases the managed resources. |
| Equals(Object) | Determines whether the specified object is equal to the current object. (Inherited from Object) |
| Fill(Span<Byte>) | Fills a span with cryptographically strong random bytes. |
| GetBytes(Byte[]) | When overridden in a derived class, fills an array of bytes with a cryptographically strong random sequence of values. |
| GetBytes(Byte[], Int32, Int32) | Fills the specified byte array with a cryptographically strong random sequence of values. |
| GetBytes(Span<Byte>) | Fills a span with cryptographically strong random bytes. |
| GetHashCode() | Serves as the default hash function. (Inherited from Object) |
| GetInt32(Int32) | Generates a random integer between 0 (inclusive) and a specified exclusive upper bound using a cryptographically strong random number generator. |
| GetInt32(Int32, Int32) | Generates a random integer between a specified inclusive lower bound and a specified exclusive upper bound using a cryptographically strong random number generator. |
| GetNonZeroBytes(Byte[]) | When overridden in a derived class, fills an array of bytes with a cryptographically strong random sequence of nonzero values. |
| GetNonZeroBytes(Span<Byte>) | Fills a byte span with a cryptographically strong random sequence of nonzero values. |
| GetType() | Gets the Type of the current instance. (Inherited from Object) |
| MemberwiseClone() | Creates a shallow copy of the current Object. (Inherited from Object) |
| ToString() | Returns a string that represents the current object. (Inherited from Object) |