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May 30, 2016 07:24 AM EDT

Randomness Extractor Generator: Computer Scientists Found Easier, More Genuine Way of Producing Random Numbers

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It seems random computer-generated numbers are not so random at all. A new method for producing genuine random numbers has been discovered by researchers at the University of Texas. The new method is also easier to procure and makes various data more secure thanks to the innovative computational technique.

New Random Numbers Extractor More Secure Than Pseudorandom Numbers

The new method called randomness extractor relies on the unpredictability of the environment, Science News reported. True randomness is important in this modern age as random numbers are crucial in the encryption of classified information on the internet, cybersecurity, determining lottery and other events that call on random numbers.

Any sort of predictable patterns in encryption can serve as a loophole for hackers via reverse engineering, BBC shared. The algorithm that requires genuinely random numbers make sure that reverse engineering will take a long time do because hackers have no predictability that they can base their pattern on.

The randomness extractor was created by David Zuckerman and Eshan Chattopadhyay, a computer science professor and undergraduate respectively at the University of Texas. They will be present their randomness extractor method next month at Symposium on the Theory of Computing (STOC) in Massachusetts.


Randomness Extractor Do Not Rely on Predictable Patterns

The randomness extractor uses two random sequences of numbers. These sequences are taken from stock market prices, measurement of natural elements and the like. However, by taking two of these random sequences, genuine random numbers can be produced.

The draft of the randomness extractor was published online a forum last year, Phys.org shared. It was well-received by their peers and thought it made a huge improvement in the procurement of genuine random numbers.

Do you think humans can ever set an algorithm for producing truly random numbers? Let us know what you think in the comments below.

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