Shrinking generator

Last updated

In cryptography, the shrinking generator is a form of pseudorandom number generator intended to be used in a stream cipher. It was published in Crypto 1993 by Don Coppersmith, Hugo Krawczyk and Yishay Mansour. [1]

Contents

The shrinking generator uses two linear-feedback shift registers. One, called the A sequence, generates output bits, while the other, called the S sequence, controls their output. Both A and S are clocked; if the S bit is 1, then the A bit is output; if the S bit is 0, the A bit is discarded, nothing is output, and the registers are clocked again. This has the disadvantage that the generator's output rate varies irregularly, and in a way that hints at the state of S; this problem can be overcome by buffering the output. The random sequence generated by LFSR can not guarantee the unpredictability in secure system and various methods have been proposed to improve its randomness [2]

Despite this simplicity, there are currently no known attacks better than exhaustive search when the feedback polynomials are secret. If the feedback polynomials are known, however, the best known attack requires less than AS bits of output. [3]

A variant is the self-shrinking generator.

An implementation in Python

This example uses two Galois LFRSs to produce the output pseudorandom bitstream. The Python code can be used to encrypt and decrypt a file or any bytestream.

#!/usr/bin/env python3importsys# ----------------------------------------------------------------------------# Crypto4o functions start here# ----------------------------------------------------------------------------classGLFSR:"""Galois linear-feedback shift register."""def__init__(self,polynom,initial_value):print"Using polynom 0x%X, initial value: 0x%X."%(polynom,initial_value)self.polynom=polynom|1self.data=initial_valuetmp=polynomself.mask=1whiletmp!=0:iftmp&self.mask!=0:tmp^=self.maskiftmp==0:breakself.mask<<=1defnext_state(self):self.data<<=1retval=0ifself.data&self.mask!=0:retval=1self.data^=self.polynomreturnretvalclassSPRNG:def__init__(self,polynom_d,init_value_d,polynom_c,init_value_c):print"GLFSR D0: ",self.glfsr_d=GLFSR(polynom_d,init_value_d)print"GLFSR C0: ",self.glfsr_c=GLFSR(polynom_c,init_value_c)defnext_byte(self):byte=0bitpos=7whileTrue:bit_d=self.glfsr_d.next_state()bit_c=self.glfsr_c.next_state()ifbit_c!=0:bit_r=bit_dbyte|=bit_r<<bitposbitpos-=1ifbitpos<0:breakreturnbyte# ----------------------------------------------------------------------------# Crypto4o functions end here# ----------------------------------------------------------------------------defmain():prng=SPRNG(int(sys.argv[3],16),int(sys.argv[4],16),int(sys.argv[5],16),int(sys.argv[6],16),)withopen(sys.argv[1],"rb")asf,open(sys.argv[2],"wb")asg:whileTrue:input_ch=f.read(1)ifinput_ch=="":breakrandom_ch=prng.next_byte()&0xFFg.write(chr(ord(input_ch)^random_ch))if__name__=="__main__":main()

See also

Related Research Articles

A pseudorandom number generator (PRNG), also known as a deterministic random bit generator (DRBG), is an algorithm for generating a sequence of numbers whose properties approximate the properties of sequences of random numbers. The PRNG-generated sequence is not truly random, because it is completely determined by an initial value, called the PRNG's seed. Although sequences that are closer to truly random can be generated using hardware random number generators, pseudorandom number generators are important in practice for their speed in number generation and their reproducibility.

<span class="mw-page-title-main">Linear congruential generator</span> Algorithm for generating pseudo-randomized numbers

A linear congruential generator (LCG) is an algorithm that yields a sequence of pseudo-randomized numbers calculated with a discontinuous piecewise linear equation. The method represents one of the oldest and best-known pseudorandom number generator algorithms. The theory behind them is relatively easy to understand, and they are easily implemented and fast, especially on computer hardware which can provide modular arithmetic by storage-bit truncation.

The Mersenne Twister is a general-purpose pseudorandom number generator (PRNG) developed in 1997 by Makoto Matsumoto and Takuji Nishimura. Its name derives from the choice of a Mersenne prime as its period length.

A Lagged Fibonacci generator is an example of a pseudorandom number generator. This class of random number generator is aimed at being an improvement on the 'standard' linear congruential generator. These are based on a generalisation of the Fibonacci sequence.

<span class="mw-page-title-main">Stream cipher</span> Type of symmetric key cipher

A stream cipher is a symmetric key cipher where plaintext digits are combined with a pseudorandom cipher digit stream (keystream). In a stream cipher, each plaintext digit is encrypted one at a time with the corresponding digit of the keystream, to give a digit of the ciphertext stream. Since encryption of each digit is dependent on the current state of the cipher, it is also known as state cipher. In practice, a digit is typically a bit and the combining operation is an exclusive-or (XOR).

In computing, a linear-feedback shift register (LFSR) is a shift register whose input bit is a linear function of its previous state.

The Yarrow algorithm is a family of cryptographic pseudorandom number generators (CSPRNG) devised by John Kelsey, Bruce Schneier, and Niels Ferguson and published in 1999. The Yarrow algorithm is explicitly unpatented, royalty-free, and open source; no license is required to use it. An improved design from Ferguson and Schneier, Fortuna, is described in their book, Practical Cryptography

<span class="mw-page-title-main">Block cipher mode of operation</span> Cryptography algorithm

In cryptography, a block cipher mode of operation is an algorithm that uses a block cipher to provide information security such as confidentiality or authenticity. A block cipher by itself is only suitable for the secure cryptographic transformation of one fixed-length group of bits called a block. A mode of operation describes how to repeatedly apply a cipher's single-block operation to securely transform amounts of data larger than a block.

A cryptographically secure pseudorandom number generator (CSPRNG) or cryptographic pseudorandom number generator (CPRNG) is a pseudorandom number generator (PRNG) with properties that make it suitable for use in cryptography. It is also referred to as a cryptographic random number generator (CRNG).

<span class="mw-page-title-main">/dev/random</span> Pseudorandom number generator file in Unix-like operating systems

In Unix-like operating systems, /dev/random and /dev/urandom are special files that serve as cryptographically secure pseudorandom number generators (CSPRNGs). They allow access to a CSPRNG that is seeded with entropy from environmental noise, collected from device drivers and other sources. /dev/random typically blocked if there was less entropy available than requested; more recently it usually blocks at startup until sufficient entropy has been gathered, then unblocks permanently. The /dev/urandom device typically was never a blocking device, even if the pseudorandom number generator seed was not fully initialized with entropy since boot. Not all operating systems implement the same methods for /dev/random and /dev/urandom.

The move-to-front (MTF) transform is an encoding of data designed to improve the performance of entropy encoding techniques of compression. When efficiently implemented, it is fast enough that its benefits usually justify including it as an extra step in data compression algorithm.

A pseudorandom binary sequence (PRBS), pseudorandom binary code or pseudorandom bitstream is a binary sequence that, while generated with a deterministic algorithm, is difficult to predict and exhibits statistical behavior similar to a truly random sequence. PRBS generators are used in telecommunication, such as in analog-to-information conversion, but also in encryption, simulation, correlation technique and time-of-flight spectroscopy. The most common example is the maximum length sequence generated by a (maximal) linear feedback shift register (LFSR). Other examples are Gold sequences, Kasami sequences and JPL sequences, all based on LFSRs.

In a pseudorandom number generator (PRNG), a full cycle or full period is the behavior of a PRNG over its set of valid states. In particular, a PRNG is said to have a full cycle if, for any valid seed state, the PRNG traverses every valid state before returning to the seed state, i.e., the period is equal to the cardinality of the state space.

<span class="mw-page-title-main">Random number generation</span> Producing a sequence that cannot be predicted better than by random chance

Random number generation is a process by which, often by means of a random number generator (RNG), a sequence of numbers or symbols that cannot be reasonably predicted better than by random chance is generated. This means that the particular outcome sequence will contain some patterns detectable in hindsight but impossible to foresee. True random number generators can be hardware random-number generators (HRNGs), wherein each generation is a function of the current value of a physical environment's attribute that is constantly changing in a manner that is practically impossible to model. This would be in contrast to so-called "random number generations" done by pseudorandom number generators (PRNGs), which generate numbers that only look random but are in fact predetermined—these generations can be reproduced simply by knowing the state of the PRNG.

CryptGenRandom is a deprecated cryptographically secure pseudorandom number generator function that is included in Microsoft CryptoAPI. In Win32 programs, Microsoft recommends its use anywhere random number generation is needed. A 2007 paper from Hebrew University suggested security problems in the Windows 2000 implementation of CryptGenRandom. Microsoft later acknowledged that the same problems exist in Windows XP, but not in Vista. Microsoft released a fix for the bug with Windows XP Service Pack 3 in mid-2008.

In cryptography, an alternating step generator (ASG) is a cryptographic pseudorandom number generator used in stream ciphers, based on three linear-feedback shift registers. Its output is a combination of two LFSRs which are stepped (clocked) in an alternating fashion, depending on the output of a third LFSR.

<span class="mw-page-title-main">Xorshift</span> Class of pseudorandom number generators

Xorshift random number generators, also called shift-register generators, are a class of pseudorandom number generators that were invented by George Marsaglia. They are a subset of linear-feedback shift registers (LFSRs) which allow a particularly efficient implementation in software without the excessive use of sparse polynomials. They generate the next number in their sequence by repeatedly taking the exclusive or of a number with a bit-shifted version of itself. This makes execution extremely efficient on modern computer architectures, but it does not benefit efficiency in a hardware implementation. Like all LFSRs, the parameters have to be chosen very carefully in order to achieve a long period.

A counter-based random number generation is a kind of pseudorandom number generator that uses only an integer counter as its internal state. They are generally used for generating pseudorandom numbers for large parallel computations.

A mask generation function (MGF) is a cryptographic primitive similar to a cryptographic hash function except that while a hash function's output has a fixed size, a MGF supports output of a variable length. In this respect, a MGF can be viewed as a extendable-output function (XOF): it can accept input of any length and process it to produce output of any length. Mask generation functions are completely deterministic: for any given input and any desired output length the output is always the same.

A permuted congruential generator (PCG) is a pseudorandom number generation algorithm developed in 2014 by Dr. M.E. O'Neill which applies an output permutation function to improve the statistical properties of a modulo-2n linear congruential generator. It achieves excellent statistical performance with small and fast code, and small state size.

References

  1. D. Coppersmith, H. Krawczyk, and Y. Mansour, “The shrinking generator,” in CRYPTO ’93: Proceedings of the 13th annual international cryptology conference on Advances in cryptology, (New York, NY, USA), pp. 22–39, Springer-Verlag New York, Inc., 1994
  2. Poorghanad, A. et al. Generating High Quality Pseudo Random Number Using Evolutionary methods IEEE, DOI: 10.1109/CIS.2008.220.
  3. Caballero-Gil, P. et al. New Attack Strategy for the Shrinking Generator Journal of Research and Practice in Information Technology, Vol. 1, pages 331–335, Dec 2008.