Generative Database Synthetic Data Generation
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Solution Overview
Problem
Businesses face challenges in providing timely data modeling and analysis due to regulatory and privacy hurdles when accessing sensitive datasets, requiring manual and time-consuming data de-sensitization processes.
Innovation Solution
A system and method for on-demand generation of anonymized and privacy-compliant synthetic datasets using a generative database, which identifies a query for synthetic data samples statistically representative of a target sensitive dataset, constructs a generative model election request, searches for an appropriate generative model based on efficacy metrics, and generates synthetic data that preserves privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data de-sensitization is performed to ensure privacy compliance, then privacy protection is improved, but processing time increases significantly
Solution Approach 1:
The patent creates synthetic copies of sensitive datasets that preserve statistical properties and relationships without containing actual sensitive information. Generative models produce artificial data samples that replicate the structure, distributions, and correlations of the original data, enabling analysis without exposing real PII or PHI.
Solution Approach 2:
The patent replaces manual mechanical de-sensitization processes with automated machine learning systems. Instead of human analysts manually identifying and removing sensitive elements, generative models automatically synthesize privacy-preserving data, dramatically reducing processing time from weeks/months to hours.
2Reliability
If manual data de-sensitization is performed to ensure privacy compliance, then privacy protection is improved, but productivity decreases
Solution Approach 1:
Synthetic data copies enable multiple analysis teams to work simultaneously on the same dataset without competing for access to sensitive information. Each team receives identical privacy-preserving synthetic data, eliminating bottlenecks and enabling parallel processing that dramatically increases overall productivity.
Solution Approach 2:
The generative database system automatically handles privacy compliance without requiring manual intervention. The system self-manages the synthesis process, model selection, and data generation, freeing personnel from time-consuming de-sensitization tasks and enabling them to focus on higher-value analysis work.
3Reliability
If synthetic data is generated to preserve privacy, then privacy compliance is improved, but data representativeness may be compromised
Solution Approach 1:
The patent employs feedback mechanisms where generative models are trained and evaluated against the original data distributions, with continuous refinement based on statistical fidelity metrics. The system monitors and adjusts synthetic data quality to ensure it maintains accurate representations of underlying data patterns while preserving privacy.
Solution Approach 2:
The system dynamically adjusts generation parameters such as sample size, diversity controls, and statistical constraints to optimize the balance between privacy protection and data representativeness. By tuning these parameters, the system can produce synthetic data with varying degrees of fidelity depending on specific analytical needs.
Data Source
AI summary
A system and method for generating synthetic datasets includes receiving, via an application programming interface (API) of a remote generative database service, a generative database query for obtaining synthetic data samples statistically representative of a sensitive dataset, searching a generative model data structure comprising a plurality of generative model nexuses based on a generative model election request derived from the generative database query, wherein the searching returns a generative model for fulfilling the generative database query, generating a synthetic dataset using the generative model returned from the searching based on a plurality of generative query parameters extracted from the generative database query, and returning the synthetic dataset as a result to the generative database query.