Anonymized Data Sampling for Secure Classification Review

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Solution Overview

Problem

Discovering and classifying target data across multiple computing systems is challenging due to the volume of data and the risk of data-related incidents such as data breaches during the transfer of data between systems.

Innovation Solution

A method involving the generation of supplemental anonymizing data samples that are fictitious occurrences of target data, intermingled with real data samples, to verify data mapping without exposing sensitive information.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If real data samples are transferred between computing systems for discovery and classification, then data mapping accuracy can be verified, but the risk of data breaches and unauthorized access increases

Engineering Contradiction:

Improvedata mapping accuracyVSAvoiddata breach risk

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The patent creates synthetic data samples that replicate the statistical properties and patterns of real data without containing actual sensitive information. These synthetic samples are used for verification purposes, allowing accuracy checking while eliminating the security risks associated with transferring real data between systems.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent introduces an intermediary processing layer that generates and manages synthetic data samples. This intermediary layer acts as a buffer between source and target computing systems, enabling verification operations without direct exposure of real data, thus mediating the security-accuracy tradeoff.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If data is discovered and classified across multiple computing systems, then comprehensive data mapping is achieved, but computing resources are significantly consumed

Engineering Contradiction:

Improvedata mapping coverageVSAvoidcomputing resource consumption

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The patent employs sampling techniques where only representative subsets of data are processed for discovery and classification rather than complete datasets. By processing partial data samples (synthetic or real), the system achieves sufficient mapping coverage with reduced computational overhead, avoiding the need to process excessive data volumes.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The patent divides the data discovery and classification process into manageable segments across multiple computing systems. Each system processes specific portions or types of data, and results are aggregated to achieve comprehensive mapping. This segmentation distributes computational load and improves resource utilization efficiency.

Inventive Principle:

Principle #1Segmentation

3Adaptability or versatility

If data is transferred between multiple computing systems for classification, then target data can be discovered across systems, but the risk of data loss events increases

Engineering Contradiction:

Improvecross-system data discoveryVSAvoiddata loss risk

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent uses synthetic data samples as copies that can be freely transferred between computing systems without the security risks associated with real data. These synthetic copies enable cross-system data discovery and classification verification while eliminating the risk of actual data loss or unauthorized access during transfer operations.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent performs preliminary generation and validation of synthetic data samples before they are used in cross-system transfers. This preliminary action ensures that the synthetic samples are properly configured and safe for transfer, preventing potential data loss events by establishing security protocols before actual data movement occurs.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12536329B2Data processing systems and methods for anonymizing data samples in classification analysis

Publication Date: 2026.01.27 ONETRUST LLC

AI summary

In general, various aspects of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for mapping the existence of target data within computing systems in a manner that does not expose the target data to potential data-related incidents. In accordance with various aspects, a method is provided that comprises: receiving a source dataset that comprises a label assigned to a data element used by a data source in handling target data that identifies a type of target data and data samples gathered for the data element; determining, based on the label, that the data samples are to be anonymized; generating supplemental anonymizing data samples associated with the label that comprise fictitious occurrences of the type of the target data; generating a review dataset comprising the supplemental anonymizing data samples intermingled with the data samples; and sending the review dataset to a review computing system.