Deidentified Production Data Testing via Alias Records

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Solution Overview

Problem

Network operators face challenges in sharing production data for testing due to the presence of sensitive information, which must be protected, while also needing data that replicates actual conditions for application and device testing to ensure operability with existing systems.

Innovation Solution

A system and method for generating deidentified production data using a deidentification engine that creates alias records by replacing identifying information with deidentified, randomized, or anonymized data, allowing for the creation of alias records that mirror real records but cannot identify users, thus protecting sensitive information.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If actual production data is used for testing, then the testing accuracy and realism are improved, but sensitive user information is exposed

Engineering Contradiction:

Improvetesting accuracyVSAvoidsensitive information exposure

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The patent creates alias records that are copies of real production records but with deidentified attributes. The alias records mirror the structure, relationships, and operational characteristics of real data while replacing sensitive identifiers with pseudonymous equivalents, enabling realistic testing without exposing actual user information

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The deidentification engine acts as an intermediary between the production data system and the testing environment. It transforms real production data into deidentified alias records that can be safely shared with third-party developers while maintaining the operational characteristics needed for accurate testing

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If production data is shared with third parties for testing, then the operability verification is improved, but data security is compromised

Engineering Contradiction:

Improveoperability verificationVSAvoiddata security risk

Core Design Contradiction:

ReliabilityVSObject-affected harmful factors

Solution Approach 1:

Instead of sharing actual production data, the system generates alias records that replicate the operational characteristics, data relationships, and transaction patterns of real production data. These copies enable comprehensive operability verification while maintaining security through deidentification

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The deidentification engine modifies specific parameters of the production data by replacing identifiable attributes with pseudonymous values while preserving the structural and operational parameters needed for testing. This transformation maintains data utility for operability verification while eliminating security risks

Inventive Principle:

Principle #35Parameter changes

3Loss of information

If real user information is used in testing, then the data completeness and accuracy are improved, but user privacy is violated

Engineering Contradiction:

Improvedata completenessVSAvoiduser privacy violation

Core Design Contradiction:

Loss of informationVSObject-affected harmful factors

Solution Approach 1:

The system creates complete alias records that replicate all fields and relationships of real user data without exposing actual user identifiers. The alias records maintain referential integrity and data relationships, ensuring completeness for testing while protecting privacy through systematic deidentification of all personally identifiable information

Inventive Principle:

Principle #26Copying

Data Source

PatentUS9892281B1Testing using deidentified production data

Publication Date: 2018.02.13 CHCA

AI summary

Systems and methods for evaluating elements of a computer network using deidentified production data are described. The production data can include a set of alias records, which include deidentified data, and can be generated from corresponding real records of actual users. Evaluating elements can include passing the production data to the elements as messages for processing.