Artificial Data Generation With Adaptive Noise for Differential Privacy
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Solution Overview
Problem
Existing methods for generating artificial data for differential privacy struggle with selecting an appropriate epsilon value and ensuring dataset-dependent quality, particularly for datasets with multiple variables, as they require knowledge of the dataset and are difficult for inexperienced analysts to implement effectively.
Innovation Solution
A system and method for configuring parameters to generate artificial data with differential privacy, adjusting noise levels based on desired privacy levels and dataset characteristics, using epsilon, upper and lower bounds, and fitting appropriate distribution types to ensure privacy and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If noise is added to parameters of fitted distribution to achieve differential privacy, then privacy preservation is improved, but data accuracy deteriorates
Solution Approach 1:
The system automatically adjusts the epsilon parameter and noise level based on dataset characteristics and privacy requirements. By changing parameters dynamically rather than using fixed values, the system optimizes the balance between privacy preservation and data accuracy for each specific dataset
Solution Approach 2:
The system performs self-configuration by automatically analyzing dataset properties and determining appropriate privacy parameters without requiring manual intervention from analysts. This automated parameter selection process helps inexperienced users achieve optimal privacy-accuracy tradeoffs
2Adaptability or versatility
If manual configuration of privacy parameters is used, then flexibility in privacy level adjustment is improved, but ease of operation deteriorates
Solution Approach 1:
The system automatically analyzes dataset characteristics and configures privacy parameters without requiring manual user input. This self-configuration capability maintains flexibility in privacy level adjustment while eliminating the operational complexity that would burden inexperienced users
Solution Approach 2:
The system incorporates automated quality assessment that provides feedback on the suitability of generated artificial data. This feedback mechanism allows the system to automatically adjust parameters to achieve desired privacy levels while maintaining data quality, without requiring manual intervention
3Ease of operation
If automated parameter selection is implemented, then ease of operation is improved, but manufacturing precision deteriorates
Solution Approach 1:
The system incorporates automated quality assessment mechanisms that evaluate the suitability of generated artificial data. This feedback loop allows the automated parameter selection process to iteratively improve and ensure that the selected parameters achieve both the desired privacy levels and maintain data quality standards
Solution Approach 2:
The system replaces manual parameter configuration with automated statistical analysis and machine learning algorithms. These computational methods objectively analyze dataset characteristics and determine optimal parameters, ensuring accuracy that may exceed manual configuration while maintaining ease of operation
4Loss of information
If statistical analysis techniques are applied to dataset, then knowledge of dataset characteristics is improved, but privacy protection deteriorates
Solution Approach 1:
The system extracts only the necessary statistical characteristics and distribution parameters from the original sensitive data, rather than analyzing or storing the actual sensitive values. This extraction approach enables the system to gain knowledge of dataset characteristics while minimizing exposure of actual private information
Solution Approach 2:
The system uses fitted statistical distributions as intermediaries between the original sensitive data and the artificial data generation process. By working with distribution parameters rather than raw data, the system gains necessary statistical knowledge while maintaining privacy protection through the intermediary layer
Data Source
AI summary
An embodiment configures a plurality of parameters, the parameters being usable to generate artificial data from original data, the configuring adjusting a level of privacy in the artificial data. An embodiment fits a distribution type to a variable of the original data. An embodiment adjusts, using a desired level of privacy and the distribution type, a level of noise, wherein the level of noise corresponds to the desired level of privacy. An embodiment generates, using the distribution type and the level of noise, the artificial data, the artificial data achieving the desired level of privacy by including noise data corresponding to the level of noise.