Automated Consumer Data Obfuscation Model
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Solution Overview
Problem
Existing methods for privatizing consumer data are inefficient and labor-intensive, particularly when dealing with large datasets, as they require manual operations and cannot be scaled effectively to preserve data characteristics for analysis.
Innovation Solution
The proposed solution involves a data privatization system that aggregates consumer information datasets, determines the degree and type of obfuscation needed based on the data characteristics, and applies obfuscation adjustments using an obfuscation model to privatize the data without significantly skewing analytical results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual operations are used to privatize consumer data, then privacy protection is achieved, but efficiency and scalability are severely limited
Solution Approach 1:
The patent replaces manual mechanical operations with an automated computer-based system that performs data privatization. The system automatically identifies personal information, applies obfuscation techniques, and generates privatized datasets without human intervention, thereby maintaining privacy protection while dramatically improving efficiency and scalability.
Solution Approach 2:
The system enables self-service by automatically performing the entire privatization process without requiring manual operations. The computer-based system independently identifies sensitive data, determines appropriate obfuscation methods, applies transformations, and validates results, allowing the process to serve itself and scale to large datasets.
2Reliability
If extensive obfuscation is applied to protect privacy, then privacy protection is improved, but data characteristics and analytical value are lost
Solution Approach 1:
The patent applies local quality by selectively obfuscating only the specific portions of data that contain personal information, while leaving other data characteristics intact. The system identifies and protects only the necessary elements (names, addresses, phone numbers) while preserving the overall structure, patterns, and analytical value of the dataset for research purposes.
Solution Approach 2:
The system changes parameters of the personal information data through various transformation techniques such as perturbation, suppression, and generalization. These parameter changes modify the data enough to protect privacy while maintaining the statistical properties and analytical characteristics needed for legitimate research and analysis.
Data Source
AI summary
Methods, systems, articles of manufacture and apparatus to privatize consumer data are disclosed. A disclosed example apparatus includes a consumer data acquirer to collect original data corresponding to (a) confidential information associated with consumers and (b) behavior information associated with the consumers, and a data obfuscator. The data obfuscator is to determine a degree to which the original data is to be obfuscated and a type of obfuscation to be applied to the original data based on the original data, generate obfuscation adjustments of the original data based on the degree and the type, and generate an obfuscation model based on the obfuscation adjustments.