Adaptive Data Obfuscation Through Machine Learning Pattern Recognition

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing data management systems lack effective methods for dynamically obfuscating sensitive data using pattern recognition techniques to protect against unauthorized access and ensure data integrity.

Innovation Solution

A system utilizing machine learning algorithms to analyze data artifacts, determine patterns, and iteratively apply obfuscation algorithms until a similarity index between masked and unmasked data meets a predetermined threshold, ensuring adequate data protection.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If traditional static data obfuscation methods are used, then data security is provided, but data utility and adaptability to different threat patterns are lost

Engineering Contradiction:

Improveadaptability of obfuscation methodsVSAvoidcomplexity of obfuscation system

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements dynamic data obfuscation by transitioning from static obfuscation methods to adaptive methods that change based on detected patterns. The system continuously monitors data access patterns and dynamically adjusts obfuscation techniques, ensuring adaptability to evolving threats while maintaining manageable complexity through automated pattern recognition.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The obfuscation system performs self-adjustment by automatically detecting patterns in data access and autonomously selecting appropriate obfuscation methods without requiring external intervention. This self-service capability enhances adaptability while keeping the system complexity contained through automated decision-making algorithms.

Inventive Principle:

Principle #25Self-service

2Reliability

If strong obfuscation algorithms are applied, then data privacy is enhanced, but data usability for legitimate analysis deteriorates

Engineering Contradiction:

Improvedata privacy protectionVSAvoidloss of data utility

Core Design Contradiction:

ReliabilityVSLoss of information

Solution Approach 1:

The system dynamically adjusts obfuscation parameters based on the detected pattern strength and data sensitivity. By changing parameters such as obfuscation intensity, transformation methods, and masking levels, the system achieves optimal balance between privacy protection and data usability for legitimate analytical purposes.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent applies partial obfuscation actions by selectively obfuscating only the portions of data that exhibit suspicious patterns while leaving other data accessible. This approach maintains data utility for legitimate analysis while providing targeted privacy protection where needed.

Inventive Principle:

Principle #16Partial or excessive action

3Measurement precision

If manual pattern analysis is performed, then obfuscation decisions are precise, but processing speed and productivity decrease

Engineering Contradiction:

Improveprecision of pattern detectionVSAvoiddata processing speed

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces manual mechanical pattern analysis with automated machine learning algorithms and artificial intelligence systems. These computational systems maintain high precision in pattern detection while dramatically increasing processing speed and productivity by eliminating human intervention in the analysis process.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system introduces intermediary automated analysis tools that bridge the gap between raw data and obfuscation decisions. These intermediaries perform precise pattern recognition and facilitate rapid decision-making, maintaining accuracy while enhancing overall processing productivity.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12361316B2System for implementing dynamic data obfuscation using pattern recognition techniques

Publication Date: 2025.07.15 BANK OF AMERICA CORP

AI summary

Systems, computer program products, and methods are described herein for implementing dynamic data obfuscation using pattern recognition techniques. The present invention is configured to electronically receive one or more data artifacts; electronically receive one or more masked data artifacts; initiate one or more machine learning algorithms on the one or more data artifacts and the one or more masked data artifacts; determine, using the one or more machine learning algorithms, a first set of patterns associated with the one or more data artifacts and a second set of patterns associated with the one or more masked data artifacts; determine a similarity index between the first set of patterns and the second set of patterns; and compare the similarity index with a predetermined threshold; determine one or more alternate data obfuscation algorithms; and implement the one or more alternate data obfuscation algorithms on the one or more data artifacts.