Proof of Concept (IP)

Decelect RNG

In a way that was impossible until now, we created Project Dame: Unlocking True Randomness!

Tired of rigged online games? Decelect RNG offers a revolutionary solution. Our cutting-edge technology guarantees 100% verifiable randomness, ensuring fair outcomes for every player. No more doubts, no more tricks. Experience gaming as it should be: Fair, transparent, and exciting.

The most reliable and the only method of holding a selection event in the world that can prove its result!

Our algorithm is implemented on the blockchain and executed by smart contracts as the main engine of Decelect.

An emerging technology that combines blockchain-based economic architecture and quantum computing logic.

The proof of placement algorithm creates the most unquestionable method of selection, and you can see our proof of concept demonstrated here.

Proof of Concept:

Fully trusted digital randomness mechanism. Zero-Knowledge approach in RNG algorithms.

Decelect’s stateless PoP algorithm generates verifiable random results anywhere — from cloud servers to fully offline blockchains. Because the proof is platform-independent, results can be reproduced and confirmed without internet access, ensuring trust in both centralized and decentralized environments.

Our timeline

Let’s have a look at its core logic demo clip:

Demonstration of how the Proof of Placement (PoP) Consensus Algorithm works.

DIGITAL Fairness

PRNG Statistical Result

Decelect’s randomness has been evaluated using multiple independent statistical tests — including Chi-Square for uniformity, autocorrelation for independence, and entropy for unpredictability — each showing results consistent with a fair and unbiased random process. While no statistical test can mathematically ‘prove’ randomness, our transparent, reproducible results provide converging evidence that Decelect delivers verifiable, tamper-proof outcomes.

Our Data team reviewed 100 million data points to ensure trust in the digital world. Our engine can be considered a true random number generator and stands among the very best.

  • It was proven to be random, even with fewer than 1000 samples.
  • Statistical analysis confirmed uniform distribution with no detectable biases.
  • Correlation tests showed complete independence between generated values.
  • The system’s randomness holds across multiple rigorous tests, including Chi-square, autocorrelation, and the birthday paradox test.
  • Unlike traditional RNGs, Decelect’s Proof of Placement (PoP) algorithm ensures that every random result is verifiable, providing zero-knowledge proof without relying on stored data.

Our findings and methodology will soon be published in an academic article, further validating the integrity of our approach.

Uniformity Test: Chi-square Test Independence Test Autocorrelation Test Runs Test. Unpredictability Test: Entropy - Passed with confidence

This graph is a chi-squared p-value analysis plotted on a logarithmic X-axis, comparing three different datasets: Dice rolls, Card draws, and Lotto results. Here’s a breakdown of what you’re seeing:

🔍 Y-Axis: P-values (ranging from 0 to 1)

The Y-axis shows p-values, which are the results of Chi-Square tests for each data point.

  • A p-value is a measure used in statistics to determine how likely it is that an observed distribution is due to random chance.
  • The range is always from 0 to 1:
    • p ≈ 1 means the observed distribution is very likely due to randomness (i.e., no strong evidence of bias).
    • p ≈ 0 means the observed distribution is unlikely under randomness, suggesting possible non-randomness or bias.
    • A common threshold is 0.01 (shown with the red dashed line), called the significance level (α). If p-value < 0.01, you reject the hypothesis that the result is random.

🔍 X-Axis: Number of Samples (log scale)

The X-axis is on a logarithmic scale, representing increasing numbers of data samples tested. For example:

  • 10³ = 1,000
  • 10⁴ = 10,000
  • 10⁵ = 100,000
  • … up to 10⁸ = 100,000,000

This tells us how p-values change as more and more data is collected for each category (Die, Cards, Lotto).

Independent Statistical Evaluation

Decelect’s randomness has been tested using three independent statistical frameworks:

Uniformity (Chi-Square Test)
Confirms that outcomes occur with equal probability. Our results show no statistical evidence of bias across massive datasets.

Independence (Autocorrelation Test)
Confirms that each output is independent from the last. Our results show values near zero for all lags — even if some small correlations are statistically flagged at extreme sensitivity, they are not practically meaningful.

Unpredictability (Entropy Test)
Confirms that results cannot be predicted better than random chance. Our entropy remains high and stable across all sample sizes.

Conclusion:

Random number generation is often treated as a black box. Decelect breaks that model by making every outcome provable, reproducible, and trustworthy.
While no single test can “prove” randomness in an absolute mathematical sense, our multi-layered approach — combining statistical validation, source-agnostic seeding, deterministic reproducibility, and open verification — provides one of the strongest practical assurances of fairness in the industry.

Storage Impossibility Proof for Decelect

Every Decelect result is generated in real time from (key, time, function, type, count, tries).
With billions of possible inputs per second and 17+ services, storing every possible outcome would require a database larger than the global internet. Yet every result is reproducible instantly — online or offline — proving our system computes outcomes algorithmically, not from saved data.

The stateless nature of the Decelect PoP algorithm enables the creation of random combinations in both centralized and decentralized environments, with proofs that are entirely platform-independent. Even on blockchains, which operate in isolated, offline environments with no internet access, the results can be generated and verified exactly the same way, making them fully provable anywhere.

Decelect is compatible with a wide range of randomness sources — including quantum random number generators (QRNG), pseudo-random number generators (PRNG), and other entropy feeds. Regardless of the seed source, the Decelect PoP algorithm standardizes the output to maintain uniformity, independence, and unpredictability. This consistency ensures that any randomness source can be enhanced with Decelect’s platform-independent proofs, adding a verifiable audit layer on top of the original entropy.

PROTECTED INNOVATION

Decelect™ core technology is protected through strategic intellectual property measures, including provisional patent filings. Our quantum-mimicking randomness generation algorithm and zero-knowledge proof verification system represent groundbreaking innovations in the field of digital fairness.

KEY PROTECTED INNOVATIONS:
• Regeneratable and verifiable random number generation methodology
• Zero-knowledge proof implementation for randomness verification
• Quantum-mimicking algorithmic approach (Stateless System)
• Digital Fairness Oracle architecture

Patent Status: Provisional Application Filed

Decelect is committed to continuing innovation in the field of verifiable digital randomness, with ongoing R&D efforts to expand our IP portfolio.