Synthetic Data Generation via Cumulative Distribution Function Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge is to generate synthetic datasets that maintain the statistical properties of actual data while ensuring privacy, especially in scenarios where sharing or using actual data is undesirable due to privacy concerns or lack of sufficient data, and existing methods like generative adversarial networks (GANs) are inefficient or less effective.
Innovation Solution
A method and system for generating synthetic datasets by creating a cumulative distribution function (CDF) model based on a source dataset, using random numbers to generate discretized synthetic data, and selecting values from the source dataset based on the CDF bins, allowing for the creation of synthetic data that mimics the statistical properties of the actual data without revealing sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If generative adversarial networks (GANs) are used to generate synthetic data, then privacy protection is improved, but computational efficiency and scalability deteriorate
Solution Approach 1:
The patent creates synthetic data by copying the statistical properties and distribution patterns from real data through CDF modeling, rather than using complex GAN training. The method generates synthetic samples that replicate the essential characteristics of real data while being computationally efficient and scalable.
2Measurement precision
If actual data is used for data science and machine learning applications, then data quality and accuracy are improved, but privacy concerns and data security deteriorate
Solution Approach 1:
The patent introduces synthetic data as an intermediary between real data and machine learning applications. This intermediary preserves the statistical properties needed for accurate modeling while eliminating privacy risks, allowing applications to operate on data that mimics real data without containing sensitive information.
3Reliability
If synthetic data is generated using complex models, then statistical properties are preserved, but device complexity and computational resources increase
Solution Approach 1:
The patent transforms the complex problem of generating statistically accurate synthetic data into a parameter-based solution using CDF modeling. By changing the approach from complex generative models to parameterized distribution functions, the method preserves statistical properties while significantly reducing computational complexity and resource requirements.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method for generating a synthetic dataset involves generating discretized synthetic data based on driving a model of a cumulative distribution function (CDF) with random numbers. The CDF is based on a source dataset. The method further includes generating the synthetic dataset from the discretized synthetic data by selecting, for inclusion into the synthetic dataset, values from a multitude of entries of the source dataset, based on the discretized synthetic data, and providing the synthetic dataset to a downstream application that is configured to operate on the source dataset.