Next Gen RNGs: Researchers Propose Model For GRNG
With the breakthrough in the world of GRNGs, this could signal the next generation for randomness, with many industries potentially benefiting from the innovation. So, let’s take a look at what RNGs are, how they have developed, and the theoretical impact of the newly proposed GRNG design.
What are RNGs?
Random number generators, or RNGs, come in many forms. Most commonly, you will find algorithm-based pseudorandom number generators (PRNGs) or hardware-based true random number generators (TRNGs). PRNGs are typically used to mimic the aspect of randomness or luck in the digital sphere. When players try their hand at mobile online casino games such as Money Blitz, Buffalo Collector, or Paddy Power Golden Odyssey, they may not know that PRNGs are the key component behind their on-demand games across a wide range of different iGaming platforms. Traditionally, casinos will have dice to be rolled, slot game levers to be pulled, wheels to be spun, and cards to be dealt. In the case of these mobile casino games, PRNGs randomly choose numbers, symbols, suits, and other characters in a way that has no sequence. This ensures that the casino experience is purely down to luck, keeping the gameplay immersive.
In higher-risk situations or those requiring enhanced randomness, TRNGs are often more appropriate. This is because they use a physical source of entropy rather than an algorithm. Whilst the algorithm of a PRNG is indeed secure and unpredictable, the nature of a physical source makes TRNGs more random and harder to crack. For this reason, TRNGs are typically used for cryptography and security applications. As the results of the TRNG are not predetermined nor based on any algorithm, this reduces the risk of hacking or successful malicious events.
From TRNGs to QRNGs
One derivation of TRNGs is quantum random number generators (QRNGs). QRNGs capitalise on the unpredictable nature of quantum physics – think Schrodinger’s cat. When a physical source serves as an input – typically an LED – the photons are simultaneously reflected and refracted. When the input is eventually picked up by sensors, their positioning cannot be predicted. It is then this positioning on the sensors that generates a random bit.
QRNGs have seen a drastic amount of innovation over the past few years due to the fact that QRNGs are often heralded as the future of cybersecurity. Today, the world’s fastest QRNG can process random numbers at a speed of 7.5Gbit/s. In addition to this, the world’s smallest QRNG is a chip measuring just 2.5mm2, which can be embedded in anything from smartphones to PCs.
The Future of TRNGs?
Whilst QRNGs are hailed by many as the future for truly unpredictable random numbers, a recent proposal by researchers from the University of Science and Technology of China takes a different approach – Gaussian random number generators (GRNG). This proposal looks at something in the world that is already random – white noise. The model known as additive white Gaussian noise (AWGN) encapsulates the phenomena of unpredictable noise sources.
With this in mind, the researchers propose utilizing AWGN using the Piecewise-CLT algorithm. Variable σ and output values were introduced into the expression derivation process of the algorithm. A scaling index was introduced to allow for the reconfiguring of the σ value. This unique approach suggests that the GRNG could be capable of an output range of ±14σ.
Whilst, at this stage, this is merely theoretical, the benefits could be exponential. The novel GRNG boasts the potential for increased flexibility, better usability, efficient reconfigurability, and improved (and reliable) outputs. In turn, this could boost the performance of simulations and security by speeding up the RNG process, improving reliability, and enhancing resource utilization.
