Quantum Random Number Generator for Differential Privacy
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Solution Overview
Problem
Existing methods for generating synthesized data for deeper analysis, such as using machine learning models, often fail to adequately privatize original data, making it difficult to maintain privacy while achieving reliable analysis results.
Innovation Solution
An electronic device that uses a quantum random number generator to produce Laplace noise, which is then used to transform first data sets into second synthesized data sets, ensuring maximum theoretical entropy and effective privatization of the original data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If noise is injected into data using pseudo-random numbers for differential privacy, then privacy protection is improved, but the quality and reliability of analysis results deteriorate due to insufficient entropy
Solution Approach 1:
The patent replaces the mechanical/algothrimic pseudo-random number generation system with a quantum physical system. Quantum random number generators use fundamental quantum phenomena (such as vacuum fluctuations or photon detection) to generate true random numbers with maximum theoretical entropy, substituting the deterministic pseudo-random approach with a fundamentally random quantum-based approach.
Solution Approach 2:
The patent changes the entropy parameter of the random noise from insufficient (pseudo-random) to maximum theoretical entropy (quantum-random). This parameter change in the noise generation process directly improves both privacy protection quality and maintains analysis result reliability by using quantum random numbers with provably higher entropy.
2Reliability
If more noise is added to achieve better privacy protection, then privacy is improved, but the usefulness and accuracy of synthesized data for machine learning deteriorates
Solution Approach 1:
The patent substitutes pseudo-random noise injection with quantum-random noise injection. The maximum theoretical entropy of quantum random numbers provides optimal privacy protection while the true randomness prevents systematic biases that could degrade data usefulness, maintaining better signal-to-noise characteristics compared to pseudo-random approaches.
Solution Approach 2:
The patent creates a composite approach by combining quantum random number generation with differential privacy noise injection. This composite method integrates the highest quality randomness source with established privacy techniques, achieving superior privacy protection while preserving data utility for machine learning applications.
3Reliability
If synthesized data is made fully privatized using traditional methods, then privacy is improved, but the ability to trace and verify original data correlations deteriorates
Solution Approach 1:
The patent replaces traditional pseudo-random privatization methods with quantum-random privatization. The maximum entropy of quantum random numbers ensures that no patterns or correlations can be traced back to original data, achieving perfect privatization while the true randomness actually prevents false correlation artifacts that can occur with pseudo-random methods.
Data Source
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AI summary
An electronic device (100) for generating synthesized data (14) that comprises a digital computer (10) being configured to receive first data sets (12) and to transform the first data sets (12) into second synthesized data sets (14) by executing a transform algorithm, wherein in the transform algorithm, Laplace noise is used as an input, further comprises a quantum random number generator (30) that is providing random quantum numbers of maximum theoretical entropy and that is communicatively coupled to the digital computer (10), the computer being configured to use quantum random numbers provided by the quantum random number generator (30) for obtaining the Laplace noise for entry into the transform algorithm