Data Type Combination Reports for Privacy-Compliant Query Results
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Data management systems face challenges in ensuring compliance with privacy laws when presenting combinations of data types to users, particularly with the proliferation of big data and evolving privacy regulations, and existing machine learning solutions are limited by expertise and computing resources.
Innovation Solution
A data management system generates a data type combination report by identifying and evaluating connections among data types using trained or heuristic models, ensuring compliance with privacy laws by reading data types from databases and providing a report that summarizes potential issues, without copying the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used to automatically discover and categorize large amounts of data, then data curation efficiency is improved, but the system becomes limited by expertise requirements and computing resources
Solution Approach 1:
The patent segments the data curation process into distinct components: data discovery, data type identification, combination evaluation, and reporting. Each component can be independently managed and optimized, reducing the overall complexity burden on the system while maintaining automated efficiency through specialized sub-processes.
Solution Approach 2:
The system performs preliminary actions by pre-defining data type categories and combination rules before actual data processing occurs. This allows the machine learning models to work within predetermined frameworks, reducing the expertise burden during runtime while maintaining high automation efficiency.
2Extent of automation
If machine learning solutions are deployed for data discovery, then automated data categorization is achieved, but false positives increase requiring additional validation
Solution Approach 1:
The patent implements feedback mechanisms where data type combination reports are generated and validated against predefined rules. This feedback loop allows the system to automatically correct false positives by comparing machine learning outputs against established data type combination criteria, maintaining high automation while improving reliability.
Solution Approach 2:
The data type combination report acts as an intermediary layer between raw machine learning data discovery results and final data categorization decisions. This intermediary validates and refines the automated outputs, reducing false positives while preserving the benefits of automation.
3Speed
If combinations of data types are presented to users without evaluation, then data access speed is improved, but privacy law compliance issues arise
Solution Approach 1:
The system performs preliminary evaluation of data type combinations against privacy law criteria before presenting data to users. By pre-establishing compliance rules and evaluating combinations in advance, the system maintains fast data access speeds while ensuring privacy law compliance is built into the data presentation process.
Solution Approach 2:
The data type combination report serves as an intermediary that evaluates and filters data type combinations before they are presented to users. This intermediary layer ensures privacy law compliance is automatically enforced without significantly impacting data access speed, as the evaluation occurs as part of the standard data retrieval process.
Data Source
AI summary
Disclosed herein are a method, system, and apparatus for appending a data type combination report to search results in response to receiving a database query to a database. When a user submits the database query to the database for a designated combination of data types, a data management system must ensure that presenting the designated combination of data types to the user does not violate applicable privacy laws. A data type combination report advises the user of any issues regarding the distribution of the results of a set of data types.