To ensure safety and trusted communication in today’s connected world, random number generators are a critical security element. The strength of the security system lies in the quality source from which the entropy is derived. The characteristics of a random number generator are:
The strength of the keys is determined by the highest degree of randomness used in its generation. This means, the higher the degree of entropy, the stronger is the key.
Random numbers are used as seeds for cryptosystems to generate keys. Hence, the strength of the keys depend on the randomness of the input seed.
There are generally two types of random number generators: deterministic random number generator, also called Pseudo-Random Number Generator (PRNG) and non-deterministic random number generators, also known as True Random Number Generator (TRNG).
PRNG is a software-based algorithm which generates random numbers from deterministic source seed. The seed for the software could be a date, temperature, pressure or any deterministic input that are given to an algorithm randomising the input by using a mathematical formula.
True Random Number Generator (TRNG) uses hardware-based inputs to create random values. Here, the inputs are generally physical processes like avalanche noise, thermal noise or atmospheric noise. These noises are then converted into electronic signals and then into digital signals in order to generate random bits.
PRNG and TRNG are vulnerable due to their predictability
· The outcome is predictable
· They can be subsequently reproduced
· In these RNG’s, the output is determined by the seed which is predictable
In cybersecurity, perfect random number is a root of trust. A QRNG does not rely on mathematical algorithms but on laws of quantum physics to ‘naturally’ generate random numbers.
A QRNG can produce unpredictable outcomes in a robust and well-controlled way. It includes the power of complex deep-tech technologies such as semiconductors, optoelectronics, high precision electronics and quantum physics that work together to create the highest level of randomness possible.
QRNGs use random properties of quantum physics to generate a true source of entropy. This improves the quality of seed for key generation. Since the entropy sources are derived from fundamental models, all the properties and behaviours are understable and provably secure.
In today’s Y2Q world, developers have to rely on the source of entropy as quantum-enabled security keys are set to become the new normal. Organisations should, therefore, implement QRNG to protect their customers’ data.