Creating sets of truly random numbers may be harder to accomplish than people think, but that hasn’t stopped UT computer science professor David Zuckerman from trying.
Computers can obtain randomness by monitoring things such as thermal noise or intervals between keystrokes, but they often fail to provide high-quality randomness, Zuckerman said.
“Think of choosing a number from 1 to 100,” Zuckerman said. “It’s perfectly random if each number has a probability of 1 in 100. [In] low-quality randomness, some of these probabilities are as high as 1 in 10.”
In a study in the Society for Industrial and Applied Mathematics, Zuckerman and a team of researchers developed an algorithm called a two-source random extractor. The algorithm takes two independent low-quality random number generators and combines them into a high-quality one. For the full article click here
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