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Unbreakable keys and increased trust for networks and devices
Provably secure Cryptography, Data and Public Key Infrastructure
Made to last Encryption and Resilience against modern attacks
Super Entropy and built with Advanced Hardware
Advancing Industrial, Scientific Aerospace and more applications
Cryptography & Data Security
Blockchain, Cryptos & Web3
Government Solutions
IoT & Authentication
Scientific & Industrial
Financial & Banking
Why are True Random numbers SO important ?
Computers, Both Algorithms and Digital Hardware Are Deterministic. And NOT Random !
Computers generate so called randomness by bitwise shifting and XOR manipulation and math formulae such as linear congruential method. Even the most sophisticated randomness function built as a computer program can be broken into and understood. Sophisticated cyber attacks determine these characteristics.
Most popular libraries generate a random sequence of 2 to the power 16, 32 or 64 numbers, and repeat the pattern. The modern computers can break codes in minutes or hours, while a Quantum computer can break it in seconds.
Randomplex attributes
Non-repeating
Unbiased
Unpredictable
Uncorrelated
Independent
Uniform
Randomplex' unique technology is derived from physical phenomena. These are not traditional computers running a deterministic algorithm to produce a number or sequence. These are also not based on any predetermined mathematically derived. The sequences are generated from high entropy sources that includes the uncertainty of quantum processes. The technology produces pure randomness with the attributes of Non-repeating, Unbiased, Unpredictable, Uncorrelated, Independent and Uniform.
While some of our statistical reports display these characteristics, our enterprise clients enjoy deeper insights and tools into our hardware cloud deployments, such as banking, financial and other sensitive sectors.
Non-repeating
Non-repeating random number generation is crucial in various applications where the uniqueness and unpredictability of each generated number are paramount. While standard random number generators (RNGs) aim for statistical randomness (uniform distribution, independence), Randomplex obeys the critical constraint that the sequence of generated numbers should not repeat within a practically relevant timeframe or at all.
Non-repeating characteristic is very important in many industries. Particularly in cryptography and cyber-security, it offers resilience.
For simulation and modeling simple variability of randomness is not sufficient. This is particularly important when simulating large number of independent events.
From lottery systems, scientific research to distributed database systems non-repetition is essential.
Unbiased
Statistical inference relies on the assumption that samples are drawn randomly and representatively from the population. Bias in the random number generation used for sampling can lead to skewed samples that do not accurately reflect the population's characteristics. This compromises the validity of statistical analyses and the conclusions drawn from them.
Many scientific, financial, and engineering models rely on random sampling to represent real-world variability. If the RNG used in these simulations is biased, the results will not accurately reflect the underlying system.
This can lead to flawed conclusions, incorrect predictions, and poor decision-making. For example, a biased RNG in a financial risk model could underestimate the likelihood of certain negative events, leading to inadequate risk management strategies.
Unpredictable
The lack of unpredictability in a random number generator can lead to severe vulnerabilities and negative consequences:
Complete Cryptographic Breakdown: If the random numbers used in encryption are predictable, even with a slight advantage, attackers can potentially compromise the entire cryptographic system, leading to the exposure of sensitive data, financial losses, and security breaches.
Exploitable Patterns in Gaming: Predictable patterns in game outcomes can be exploited by individuals or sophisticated algorithms to gain an unfair advantage, undermining the fairness and economic model of the game.
Skewed Simulation Results: Predictable "randomness" can lead to simulations that do not accurately reflect real-world variability, resulting in flawed insights and poor decision-making based on those simulations.
Compromised Security Protocols: Predictable challenges or responses in security protocols can allow attackers to bypass authentication mechanisms and gain unauthorized access.
Weakened Password Security: Predictable salts make passwords more vulnerable to dictionary attacks, even if the underlying password itself is strong.
Loss of Trust and Credibility: If a system that relies on randomness is found to use a predictable generator, it can severely damage the trust of users, customers, or the public.
Uncorrelated
Security in Cryptography (Subtle but Important): While the primary concerns in cryptography are true randomness and unpredictability, correlation can subtly undermine security. If the output of a cryptographic RNG exhibits correlation, it might reveal underlying patterns or dependencies that could be exploited by an attacker, even if the numbers appear statistically random in other aspects. For example, a slight correlation could make it easier to predict future bits based on past bits.
Fairness in Gaming and Lotteries: While direct correlation might be less obvious than bias, subtle correlations in the output of a game's RNG could theoretically be exploited by someone looking for patterns, even if the overall distribution appears fair. Uncorrelated numbers ensure that each outcome is truly independent and unpredictable.
Reliability in Stochastic Algorithms: Many computational algorithms rely on random choices to explore solution spaces efficiently (e.g., Monte Carlo methods, evolutionary algorithms). Correlation in the random numbers used can lead to inefficient exploration, premature convergence to suboptimal solutions, or biased sampling of the search space.
In the context of Randomplex Quantum hybrid true random number generator, correlation measurements is used to verify that the combination of thermal noise, shot noise, and quantum tunneling sources results in a final output stream where the numbers are statistically independent of each other, both within the sequence (autocorrelation) and also between the contributions of different entropy sources.
We demonstrated low correlation across various tests and lags are a strong indicator of the high quality and robustness of Randomplex RNG.
Independent
Imagine flipping a fair coin multiple times. Each coin flip is an independent event. The outcome of one flip (heads or tails) has no bearing on the outcome of any other flip. Similarly, an independent random number generator should produce numbers as if each draw is a fresh, new start, completely disconnected from the history of the numbers it has already produced.
Randomplex quantum hybrid true random number generator aims to produce independent random numbers by leveraging physically independent sources of noise (thermal, shot, and quantum tunneling). If the underlying physical processes generating these noises are truly independent and the combination method doesn't introduce dependencies, the resulting random numbers should also be independent. This is a crucial property for the security and reliability of applications using these numbers.
Uniform
Random numbers should ideally be uniform because this property ensures that every possible value within the defined range of the generator has an equal probability of being produced. This equi-distribution is fundamental for achieving fairness, accuracy, and reliability in the vast array of applications that rely on randomness.
In essence, uniformity provides a baseline of predictability at the distributional level, ensuring that the randomness is spread evenly across the possible outcomes.
This lack of inherent preference for any particular value is what allows us to build fair, accurate, and reliable systems on top of random processes
RandomPlex quantum hybrid true random number generators, produce numbers that not only exhibit independence and unpredictability but also follow a uniform distribution across its output range. Statistical tests for uniformity (like the Chi-squared test or Kolmogorov-Smirnov test) prove this crucial property. If the output were biased towards certain values, it would limit the generator's utility and potentially introduce flaws in applications relying on it.
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