Random Number Generator: Study Finds New System to Make Lottery, Computer Security UnpredictableBy Anita V
Random number generator research paper by University of Texas is a remarkable new method to generate random numbers that can improve how computer security is doing its job. The generated random numbers have been an important element in any encryption. It has also been used in gambling, lottery systems and many scientific research models.
The method that random numbers generator uses today, can actually be predicted. However, with the new finding, researchers claim that it results high quality random numbers but requires less computing process.
According to computer scientist at University of Texas, David Zuckerman, the current random number generator is predictable because a computer works like a calculating machine. It is able to provide results after doing the maths, Threatpost reported.
Random Number Picker is not all random
According to the professor, there are two types of random number generators.
1. A computer picks numbers using generated list or using algorithm base. It is fast but it is not that random since results are predictable.
2. Taking an unpredictable element from the world and turns it to algorithm. This system has been used to measure levels in radiation, air temperature, or noise. This also provides predictable results.
New random number generator uses weak random sources
The paper that Zuckerman and Eshan Chattopadhyay suggests is to use the new solution, that is taking two weak sources to produce one high quality random number. Two low quality sources with no correlations, are combined to generate high quality random number. It will have a faster process and more practical.
For 20 years, Zuckerman has been working on the issue and he is excited to finally present his discovery at Symposium on Theory of Computing, in June, Tom's Hardware confirmed. According to Zuckerman, researchers have been trying to find a practical way to generate the random number but the findings apparently used moderate sources instead of low quality sources.