Sensitive Data Detection Using Type-Specific Data-Segment Validation

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Solution Overview

Problem

Existing technologies lack effective methods to accurately identify sensitive data types within larger data sets, such as credit card numbers, for security purposes.

Innovation Solution

Implementing type-specific validation mechanisms to analyze data sets for the presence of sensitive data by matching data segments to corresponding data type formats, allowing for statistical inference on the presence of sensitive data types.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If type-specific validation mechanisms are applied to data segments matching data type formats, then measurement precision of sensitive data identification is improved, but device complexity increases due to multiple validation schemes

Engineering Contradiction:

Improvesensitive data identification accuracyVSAvoidvalidation mechanism complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the data set into individual data segments and applies validation mechanisms to each segment independently. This allows the system to handle complex validation requirements by breaking down the overall task into manageable pieces, where each data segment is validated against its corresponding data type format separately, thereby improving identification accuracy without overwhelming system complexity

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent changes the parameter of validation from simple format matching to statistical inference based on multiple validation results. By accumulating validation outcomes across multiple data segments and applying statistical thresholds, the system achieves high measurement precision while managing complexity through parameter-based decision making rather than complex rule sets

Inventive Principle:

Principle #35Parameter changes

2Reliability

If statistical inference is used to determine presence of sensitive data types, then reliability of sensitive data detection is improved, but loss of time increases due to cumulative analysis process

Engineering Contradiction:

Improvesensitive data detection reliabilityVSAvoiddata analysis time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent performs preliminary format matching on data segments before applying the full validation mechanism. By first identifying segments that match data type formats and then applying validation only to those candidates, the system reduces the overall analysis time while maintaining high reliability through the two-stage filtering process

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent applies validation mechanisms to more data segments than strictly necessary by analyzing all format-matching segments rather than stopping at the first few matches. This excessive action approach improves reliability through larger sample sizes for statistical inference, while the partial application to only format-matching segments keeps time loss manageable

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12373599B2Sensitive data detection

Publication Date: 2025.07.29 SOPHOS LTD

AI summary

A data set can be analyzed for the presence of sensitive data using type-specific validation mechanisms to test data within the data set that superficially matches a corresponding data type format. In general, a type-specific validation mechanism may be applied to data segments within the data set when they match the data type format, and used to cumulatively build a statistical inference about whether the data set contains the corresponding data type. This technique may usefully be applied in a range of security contexts, such as characterizing data at rest or detecting leakage of sensitive data during a data transmission.