Root of trust giving the perfect random numbers
Tropos does not rely on mathematical algorithms; it generates random numbers
from a quantum source, making it suitable for all applications.
Traditional random number generators like PRNG and TRNG use predictable inputs which are deterministic. These inputs have higher probability of repeating which creates predictability. Hence, making the entire system vulnerable.
Tropos quantum random number generator uses the principles of quantum mechanics to generate truly random numbers. By fact, quantum physics is fundamentally random in nature and is confirmed by theory and experimental research.
Quantum Random Number Generator is a highly-sophisticated engineering innovation which involves the power of complex deep-tech technologies (such as semiconductors, optoelectronics, high precision electronics and quantum physics) working together to create the highest level of randomness possible.
Applications today require a high rate of keys and randomness to ensure complete security. These could be across applications like key vaults, gaming, IOT devices, AI/ML, block chains, simulations and critical infrastructure.
Tropos QRNG forms the source of these applications where the trust on randomness is paramount. With true entropy and high rate of generation, it fulfils today’s need for a perfect source.
“Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin.”
JOHN VON NEUMANN (1951)
A laser-based quantum source generates the randomness in Tropos quantum random number generator.
To elaborate on the process, a laser produces a stream of the elementary particle, photon. The photons generated from the laser are used to generate the random numbers.
These photons unlike classical objects are unpredictable under certain situations. When incident on a semi-transparent mirror, the photon has a 50/50 chance of being reflected or transmitted. The photon is then in a superposition of both the states (reflected and transmitted), i.e. the photon exists in both the states simultaneously. Upon measurement, it collapses to one of these states, which is intrinsically random and there is no way to predict which state the photon will collapse to. This gives the inherent randomness from the photons, which cannot be influenced by any external parameters. This process is illustrated in the diagram
The following diagram depicts the process from photon generation to random number output. The process starts with the generation of light from a laser source, which is converted into single-photon level using attenuators. The photons are then sent onto a semi-transparent mirror for the superposition phenomenon and are detected using SPD (Single Photon Detector). They are then converted into bits of 1’s and 0’s, depending on the clicks generated on the SPD. Then there is post-processing in FPGAs to do the conditioning, statistical checks and then deliver the random numbers to the outside world.
The test suits check the randomness of the bits. Only if the conditions are met, they are forwarded to the applications or users requesting the random numbers.
RESTful interface is the typical API used for the transfer of random numbers from Tropos to external applications or end users.
Random numbers are used as seed in cryptosystems to generate keys. The strength of these keys depends on randomness of the input seed.
In today’s times, Pseudo Random Number Generator (PRNG) is commonly used, which is essentially a software-based algorithm starting from a seed number that produces subsequent random values which are then converted to random numbers. The seed for the software could be a date, temperature, pressure or any deterministic input that are given to the algorithm randomising the input by using a mathematical formula.
There is also True Random Number Generator (TRNG), which uses hardware-based inputs to create random values. In TRNG, the inputs are generally physical processes like avalanche noise, thermal noise or atmospheric noise. These noises are then converted into electronic signals, and thereafter into digital signals in order to generate random bits.
PRNG and TRNG are vulnerable due to their predictability. Tropos QRNG is the perfect random key generator as it uses intrinsic qualities of quantum physics to generate entropy.
For the Proof of Concept (POC) on how Quantum Random Number Generators are truly random and
their outcomes are 100% unpredictable
Perfect Random Keys
High Rate of Entropy
High Throughput Key Rates
Multiple Application Usage
Securing data at rest in data centres
Securing data in the cloud
One-time pad for authentication in banking and other transactions
Gaming applications and lottery
Block chain network
Numerical simulations, statistical research
Quantum random number generator technology entails a multitude of use cases in various verticals. The wide applicability enables public and private sectors to integrate it in a proprietary fashion while using the highest levels of entropy to achieve use case optimization.
Quantum Random Number Generator can be applied to the random routing of military weapons, equipment and supplies thereby preventing enemies from pinning the locations of the used routes. This randomization of routing can be applied for land, air and water transportation routes with in-between nodal points, as well as randomization of routes between the nodes.
QRNG can be applied to in-flight pilot simulation. This is an important use case as it optimizes the training simulations with event randomization during a simulated operation. Flight simulation with EaaS enables Air Force pilots to engage with different simulated scenarios before conducting operations, airdrops, or landing.
The Quantum Random Number Generator is applied to generate keys for confidential transactions or top-secret messages, in addition to systems for launching operations (i.e. Command Centre) or missile launch systems.
QRNG technology can be applied to ID card systems to generate a random number at every instant an ID card is used, and subsequently track the movements of every personnel (especially at defence facilities), to curtail any unnecessary movement/track specific personnel.
One of the main use cases is One Time Password (OTP), essential in the commercial sectors like banking. OTPs with a high level of entropy can be generated and sent to customers for all financial transactions, preventing fraud, as well as the on-boarding of new customers.
Random transaction IDs can be created for easy tracking and will prevent hackers or adversaries from exfiltration data.
The strength of sampling is in selecting random inputs. This can be applied in statistical analysis to select random samples to optimize the study of the hypothesis with a high degree of sample randomness. This approach is also applicable in randomizing test variables in R&D as it shortens the timeframe in selecting inputs.
QRNG can generate the inputs randomly and run tests at a higher speed. Though this is mainly used for black-box testing where the output is known, researchers, too, can apply it to know the responsible inputs.
While providing certifications, education institutes can randomize question samples to prevent the questionnaires from being passed on to other classes. This will help them avoid any cheating or malpractice, and ensure that students are capable of passing the certification’s final exams with no possibility of external interference.
It can also be used in university labs to randomly allocate test samples. This raise the learners’ chances of experimenting with non-duplicated samples, and encourage them to think independently.
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