Two researchers from the University of Texas have published a paper detailing a new algorithm for combining two sources of entropy to achieve better quality random numbers that can be used to enhance encryption tasks with less use of computing resources.
The world of computer science and cryptography is abuzz with discussion about the “Explicit Two-Source Extractors and Resilient Functions” study, published in July 2015 but recently updated in March, which details a theoretical breakthrough in relation to random number generation.
For a long, long time, the weakness in CSPRNGs (Cryptographically-Secure Pseudo-Random Number Generators) was the origin of the random number, called the entropy or entropy pool.
In most cases, for many computer systems, this is obtained from user mouse movements, keyboard inputs, disk IO events, signal interruptions, network packet inter-arrival times, or other hardware-.
Depending on the existing entropy pool at the given moment when a cryptographic system "draws" the random number for its operations, the source of that number indirectly dictates the quality of the encryption. Most attacks on cryptographic systems rely on weak random number sequences and sources.
What the two researchers have managed to do is devise an algorithm that eliminates the need for a high-quality source of random numbers. The algorithm allows programmers, in theory for now, to merge two lower-quality sources and obtain a high-quality number.
The work of David Zuckerman, a computer science professor, and Eshan Chattopadhyay, a graduate student, both at the University of Texas, was welcomed with open arms by the computer science.
Xin Li, an Assistant Professor in the Department of Computer Science at Johns Hopkins University, has already begun working to make the algorithm work with more sources.
Considering that this algorithm is resource-light and powerful at the same time, its technical implementation may only be a matter of time, with countless smartphones and IoT devices awaiting the security boost.
