A pseudorandom process produces predictable outcomes given information which is typically difficult to acquire; absent such information, pseudorandom sequences of numbers exhibit statistical randomness.
In general, a random process generates unpredictable outcomes: for any single event any particular outcome cannot be predicted in advance given available information. For example, consider an unbiased coin which on any given flip is either heads or tails: on a single flip no outcome is certain. Recording 1,000 flips in a logbook provides a sequence of pseudorandom outcomes: in possession of the logbook each outcome is known for certain; however, a person without the logbook sees only a random string of heads and tails.
To generate random numbers that can never be predicted by any observer requires a causally nondeterministic process where events are not fully determined by prior states (e.g., whether a photon is emitted by an atom in any given nanosecond). Due to the physical impossibility of acquiring sufficient information to predict the outcome of such an event, its outcomes are guaranteed to be random to all.
Randomness is therefore a condition which holds of a sequence relative to the information available to the predictor, with pseudorandomness indicating that information sufficient to predict the next outcome may be acquired by the predictor under some circumstances. The most prominent example is the pseudorandom number generators used by digital computers in which knowing a starting "seed" number produces an entirely predictable string of numbers which are unpredictable without it.
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✪ Pseudorandom number generators  Computer Science  Khan Academy

✪ How to Generate Pseudorandom Numbers  Infinite Series

✪ Pseudorandomness  Exponential sums, equidistribution and pseudorandomness  Jean Bourgain
Transcription
(fun music) (leaves rustling)  [Voiceover] When we observe the physical world we find random fluctuations everywhere. We can generate truly random numbers by measuring random fluctuations, known as noise. When we measure this noise, known as sampling, we can obtain numbers.  [Voiceover] One, two, three, four  [Voiceover] For example, if we measure the electric current of TV static over time, we will generate a truly random sequence. We can visualize this random sequence by drawing a path that changes direction according to each number, known as a random walk. Notice the lack of pattern at all scales. At each point in the sequence the next move is always unpredictable. Random processes are said to be nondeterministic, since they are impossible to determine in advance. Machines, on the other hand, are deterministic. Their operation is predictable and repeatable. In 1946, John von Neumann was involved in running computations for the military; specifically involved in the design of the hydrogen bomb. Using a computer called the ENIAC, he planned to repeatedly calculate approximations of the processes involved in nuclear fission. However, this required quick access to randomly generated numbers that could be repeated, if needed. However, the ENIAC had very limited internal memory; storing long random sequences was not possible. So, Neumann developed an algorithm to mechanically simulate the scrambling aspect of randomness as follows: First, select a truly random number, called the "seed". This number could come from the measurement of noise, or the current time in milliseconds. Next, this seed is provided as input to a simple calculation. Multiply the seed by itself, and then output the middle of this result. Then you use this output as the next seed, and repeat the process as many times as needed. This is known as the middlesquares method and is just the first in a long line of pseudorandom number generators. The randomness of the sequence is dependent on the randomness of the initial seed only. Same seed, same sequence. So, what are the differences between a randomly generated versus pseudorandomly generated sequence? Let's represent each sequence as a random walk. They seem similar until we speed things up. The pseudorandom sequence must eventually repeat. This occurs when the algorithm reaches a seed it has previously used, and the cycle repeats. The length, before a pseudorandom sequence repeats, is called "the period". The period is strictly limited by the length of the initial seed. For example, if we use a twodigit seed, then an algorithm can produce, at most, 100 numbers, before reusing a seed and repeating the cycle. A threedigit seed can't expand past 1,000 numbers before repeating its cycle, and a fourdigit seed can't expand past 10,000 numbers before repeating. Though if we use a seed large enough, the sequence can expand into trillions and trillions of digits before repeating. Though the key difference is important. When you generate numbers pseudorandomly, there are many sequences which cannot occur. For example, if Alice generates a truly random sequence of 20 shifts, it's equivalent to a uniform selection from the stack of all possible sequences of shifts. This stack contains 26 to the power of 20 pages, which is astronomical in size. If we stood at the bottom and shined a light upwards, a person at the top would not see the light for around 200,000,000 years. Compare this to Alice generating a 20 digit pseudorandom sequence, using a fourdigit random seed. Now, this is equivalent to a uniform selection from 10,000 possible initial seeds, meaning she can only generate 10,000 different sequences, which is a vanishingly small fraction of all possible sequences. When we move from random to pseudorandom shifts, we shrink the key space into a much, much smaller seedspace. So, for a pseudorandom sequence to be indistinguishable from a randomly generated sequence, it must be impractical for a computer to try all seeds and look for a match. This leads to an important distinction in computer science, between what is possible, versus what is possible in a reasonable amount of time. We use the same logic when we buy a bike lock. We know that anybody can simply try all possible combinations, until they find a match and it opens. But it would take them days to do so. So, for eight hours we assume it's practically safe. With pseudorandom generators, the security increases as the length of the seed increases. If the most powerful computer would take hundreds of years to run through all seeds, then we safely can assume it's practically secure, instead of perfectly secure. As computers get faster the seed size must increase accordingly. Pseudorandomness frees Alice and Bob from having to share their entire random shift sequence in advance. Instead, they share a relatively short random seed, and expand it into the same randomlooking sequence when needed. But what happens if they can never meet to share this random seed?
Contents
History
The generation of random numbers has many uses (mostly in statistics, for random sampling, and simulation). Before modern computing, researchers requiring random numbers would either generate them through various means (dice, cards, roulette wheels,^{[1]} etc.) or use existing random number tables.
The first attempt to provide researchers with a ready supply of random digits was in 1927, when the Cambridge University Press published a table of 41,600 digits developed by L.H.C. Tippett. In 1947, the RAND Corporation generated numbers by the electronic simulation of a roulette wheel;^{[1]} the results were eventually published in 1955 as A Million Random Digits with 100,000 Normal Deviates.
Almost random
A pseudorandom variable is a variable which is created by a deterministic algorithm, often a computer program or subroutine, which in most cases takes random bits as input. The pseudorandom string will typically be longer than the original random string, but less random (less entropic in the information theory sense). This can be useful for randomized algorithms.
Pseudorandom number generators are widely used in such applications as computer modeling (e.g., Markov chains), statistics, experimental design, etc.
Linux uses various system timings (like user keystrokes, I/O, or leastsignificant digit voltage measurements) to produce a pool of random numbers. It attempts to constantly replenish the pool, depending on the level of importance, and so will issue a random number.
In computational complexity
In theoretical computer science, a distribution is pseudorandom against a class of adversaries if no adversary from the class can distinguish it from the uniform distribution with significant advantage.^{[2]} This notion of pseudorandomness is studied in computational complexity theory and has applications to cryptography.
Formally, let S and T be finite sets and let F = {f: S → T} be a class of functions. A distribution D over S is εpseudorandom against F if for every f in F, the statistical distance between the distributions f(X), where X is sampled from D, and f(Y), where Y is sampled from the uniform distribution on S, is at most ε.
In typical applications, the class F describes a model of computation with bounded resources and one is interested in designing distributions D with certain properties that are pseudorandom against F. The distribution D is often specified as the output of a pseudorandom generator.
Cryptography
Though random numbers are needed in cryptography, the use of pseudorandom number generators (whether hardware or software or some combination) is insecure. When random values are required in cryptography, the goal is to make a message as hard to crack as possible, by eliminating or obscuring the parameters used to encrypt the message (the key) from the message itself or from the context in which it is carried. Pseudorandom sequences are deterministic and reproducible; all that is required in order to discover and reproduce a pseudorandom sequence is the algorithm used to generate it and the initial seed. So the entire sequence of numbers is only as powerful as the randomly chosen parts—sometimes the algorithm and the seed, but usually only the seed.
There are many examples in cryptographic history of ciphers, otherwise excellent, in which random choices were not random enough and security was lost as a direct consequence. The World War II Japanese PURPLE cipher machine used for diplomatic communications is a good example. It was consistently broken throughout World War II, mostly because the "key values" used were insufficiently random. They had patterns, and those patterns made any intercepted traffic readily decryptable. Had the keys (i.e., the initial settings of the stepping switches in the machine) been made unpredictably (i.e., randomly), that traffic would have been much harder to break, and perhaps even secure in practice.^{[3]}
In security
Since pseudorandom numbers are in fact deterministic, a given seed will always determine the same pseudorandom number. This attribute is used in security, in the form of rolling code to avoid replay attacks, in which a command would be intercepted to be used by a thief at a later time.^{[4]}
Monte Carlo method simulations
A Monte Carlo method simulation is defined as any method that utilizes sequences of random numbers to perform the simulation. Monte Carlo simulations are applied to many topics including quantum chromodynamics, cancer radiation therapy, traffic flow, stellar evolution and VLSI design. All these simulations require the use of random numbers and therefore pseudorandom number generators, which makes creating randomlike numbers very important.
A simple example of how a computer would perform a Monte Carlo simulation is the calculation of π. If a square enclosed a circle and a point were randomly chosen inside the square the point would either lie inside the circle or outside it. If the process were repeated many times, the ratio of the random points that lie inside the circle to the total number of random points in the square would approximate the ratio of the area of the circle to the area of the square. From this we can estimate pi, as shown in the Python code below utilizing a SciPy package to generate pseudorandom numbers with the MT19937 algorithm. Note that this method is a computationally inefficient way to numerically approximate π.
import scipy
N=100000
x_array = scipy.random.rand(N)
y_array = scipy.random.rand(N)
# generate N pseudorandom independent x and yvalues on interval [0,1)
N_qtr_circle = sum(x_array**2+y_array**2 < 1)
# Number of pts within the quarter circle x^2 + y^2 < 1 centered at the origin with radius r=1.
# True area of quarter circle is pi/4 and has N_qtr_circle points within it.
# True area of the square is 1 and has N points within it, hence we approximate pi with
pi_approx = 4*float(N_qtr_circle)/N # Typical values: 3.13756, 3.15156
See also
 Pseudorandom ensemble
 Pseudorandom binary sequence
 Pseudorandom number generator
 Quasirandom sequence
 List of random number generators
 Random number generation
References
 ^ ^{a} ^{b} "A Million Random Digits". RAND Corporation. Retrieved 20170330.
 ^ Oded Goldreich. Computational Complexity: A Conceptual Perspective. Cambridge University Press. 2008.
 ^ AlbertoPerez. "How the U.S. Cracked Japan's 'Purple Encryption Machine' at the Dawn of World War II". io9. Retrieved 20170330.
 ^ Brain, M., "How Remote Entry Works", HowStuffWorks Auto Auto Basics. Retrieved July 8, 2018.
Further reading
 Donald E. Knuth (1997) The Art of Computer Programming, Volume 2: Seminumerical Algorithms (3rd edition). AddisonWesley Professional, ISBN 0201896842
 Oded Goldreich. (2008) Computational Complexity: a conceptual perspective. Cambridge University Press. ISBN 9780521884730.^{[page needed]} (Limited preview at Google Books)
 Vadhan, S. P. (2012). "Pseudorandomness". Foundations and Trends in Theoretical Computer Science. 7: 1–336. doi:10.1561/0400000010.
External links
 HotBits: Genuine random numbers, generated by radioactive decay
 Using and Creating CryptographicQuality Random Numbers
 In RFC 1750, the use of pseudorandom number sequences in cryptography is discussed at length.