Intelligent Data Sampling for Accurate PII Detection

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing systems fail to efficiently identify and safeguard personally identifiable information (PII) across diverse and scattered data systems due to reliance on keyword searches, which are inadequate for new forms and locations, leading to potential exposure.

Innovation Solution

Implement intelligent sampling techniques using recency and lineage criteria to narrow data search parameters, identify PII, and apply security policies efficiently.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If traditional keyword search methods are used to identify PII, then the system can find known forms of personal information, but it fails to identify new forms or PII stored in different locations

Engineering Contradiction:

Improveability to identify new forms of PIIVSAvoidaccuracy of PII identification

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The patent transforms the static keyword matching approach into a dynamic data structure analysis approach. Instead of searching for fixed keywords, the system analyzes data structures, their relationships, and metadata to identify PII. This parameter change from keyword-based to structure-based identification enables the system to adapt to new forms of PII while maintaining high accuracy through systematic analysis of data lineage and relationships.

Inventive Principle:

Principle #35Parameter changes

2Reliability

If comprehensive data scanning is performed to ensure all PII is identified, then identification completeness improves, but computational resources and time consumption increase significantly

Engineering Contradiction:

Improvecompleteness of PII identificationVSAvoidcomputational efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent segments the data system into hierarchical levels (source systems, data structures, data elements) and relationships. By dividing the scanning task into manageable segments organized by data structure hierarchy, the system can efficiently process data in discrete units while maintaining comprehensive coverage. This segmentation enables parallel processing and reduces the computational burden of scanning entire data systems.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent performs preliminary actions by establishing data structure catalogs, defining relationships between data elements, and creating metadata frameworks before the actual PII identification process. This preliminary structuring of data allows the system to quickly navigate and identify PII without performing exhaustive scans, thereby improving computational efficiency while ensuring complete identification.

Inventive Principle:

Principle #10Preliminary action

3Reliability

If manual tagging of personal information is performed, then data can be safeguarded with specific tags, but the process is time-consuming and may miss untagged information

Engineering Contradiction:

Improvedata safeguarding effectivenessVSAvoidtime for manual tagging

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent implements self-service by enabling the system to automatically identify and tag PII through data structure analysis without requiring manual intervention. The system uses predefined data structure catalogs and relationship mappings to autonomously detect PII, apply appropriate tags, and enforce security policies. This automated self-service approach eliminates time-consuming manual tagging while ensuring consistent and comprehensive identification of all PII.

Inventive Principle:

Principle #25Self-service

4Ease of manufacture

If simple dictionary lookup with keywords is used, then the implementation is straightforward and quick, but it cannot account for PII in new forms or different locations

Engineering Contradiction:

Improveease of implementationVSAvoidability to handle new PII forms

Core Design Contradiction:

Ease of manufactureVSAdaptability or versatility

Solution Approach 1:

The patent creates a universal data structure catalog and relationship framework that can handle multiple types of PII across different forms and locations. The system uses a unified approach based on data structure analysis that works across diverse data types, storage locations, and PII formats. This universal framework maintains ease of implementation through standardized processes while providing the versatility to identify PII in any form or location.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20260111602A1Data detection using intelligent sampling

Publication Date: 2026.04.23 NETFLIX INC

AI summary

A computer-implemented method includes determining that a specific type of information is to be identified in a set of data. The method further includes sampling the set of data according to various sampling criteria to identify the specified type of information. The sampling criteria include at least a lineage criterion indicating that the data to be sampled is within a maximum hierarchical distance from a source data structure. The method also includes identifying, from the data that was sampled according to the sampling criteria, one or more data structures that include the specified type of information. The method further includes applying security policies to the identified data structures based on the type of information that was identified in the set of data. Various other methods, systems, and computer-readable media are also disclosed.