Database Query Risk Detection via Joint Data Classification
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Solution Overview
Problem
Existing database systems fail to effectively identify and alert on elevated risk levels of sensitive data disclosure when queries combine data elements with lower individual sensitivity ratings, leading to accidental recombination and non-compliance with privacy and security frameworks.
Innovation Solution
The system determines a risk level for database queries by parsing and classifying data elements based on categorization rules, generating a risk level alert when the combined sensitivity exceeds predetermined thresholds, and providing differential risk scoring to prevent sensitive data disclosure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data elements are stored separately with lower individual sensitivity ratings, then the risk of disclosure of individual data elements is reduced, but the risk of accidental recombination increases
Solution Approach 1:
The system performs preliminary classification and risk assessment of data elements before they are queried or combined. By pre-tagging data elements with sensitivity classifications and pre-identifying potential recombination risks, the system can detect and alert on dangerous combinations before they occur, preventing accidental recombination of sensitive data while allowing separate storage of data elements
Solution Approach 2:
The system implements continuous feedback monitoring of database queries by analyzing query patterns, data element combinations, and risk scores in real-time. When a query combines data elements to create a high-risk situation, the system provides immediate feedback through alerts and notifications, enabling operators to take corrective action before sensitive information is disclosed
2Measurement precision
If data categorization rules are made comprehensive to cover all privacy regulations, then compliance accuracy is improved, but the complexity of the system increases
Solution Approach 1:
The system segments the complex task of multi-regulation compliance into manageable components by creating modular classification rules for different privacy frameworks (HIPAA, GDPR, CCPA, PCI DSS). Each regulation is handled as a separate, independently configurable module with its own data classification criteria, making the system easier to implement, maintain, and update while maintaining comprehensive compliance coverage
Solution Approach 2:
The system uses configurable parameters and thresholds to adapt to different regulatory requirements without changing the core system architecture. By allowing administrators to adjust sensitivity thresholds, classification criteria, and risk levels through parameter changes rather than code modifications, the system maintains high compliance accuracy across multiple regulations while keeping implementation complexity manageable
3Reliability
If the system performs joint classification of union of data elements, then the detection of high-risk combinations is improved, but the processing time increases
Solution Approach 1:
The system performs preliminary classification of individual data elements and pre-computes risk scores for common data combinations. By caching classification results and pre-identifying high-risk data element pairs, the system can quickly assess query risks without performing complete joint classification analysis on every query, significantly reducing processing time while maintaining high risk detection accuracy
Solution Approach 2:
The system applies partial joint classification only to queries that involve data elements with potential recombination risks, rather than performing full classification on all queries. By using heuristics to identify queries that warrant detailed analysis and applying simplified risk assessment to low-risk queries, the system achieves high detection accuracy for dangerous combinations while minimizing overall processing time
Data Source
AI summary
A method and system are configured for identifying a risk level of data in a response to a query made to a database. The method may include receiving the query, receiving a framework including data categorization rules for the data, parsing the query to determine queried tables of data elements in the database, classifying the data elements in the queried tables based on rules in the framework to produce classification labelings, jointly classifying a union of data elements in the queried tables to produce classification labelings for the union of data elements, determining a risk level for each of the queried tables by comparing the classification labelings for data elements in each table to the classification labelings in the union of data elements, and presenting a risk level alert for the query when the risk level of any of the queried tables is above a predetermined risk level.