Pseudonym Association Model for Privacy-Preserving Data Expansion

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Solution Overview

Problem

Companies face challenges in utilizing third-party data for targeted advertising due to stringent personal information protection measures, making it difficult to secure and utilize customer data effectively.

Innovation Solution

A system and method that utilize pseudonym association to combine and estimate third-party data with first-party data, generating an interpretation model to provide expanded data for enhanced advertising targeting and personalized recommendations while protecting individual privacy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If third-party data is utilized using cookies or data connection methods, then customer data understanding is improved, but personal information protection measures make data securing difficult

Engineering Contradiction:

Improvecustomer data understandingVSAvoiddata securing feasibility

Core Design Contradiction:

Loss of informationVSReliability

Solution Approach 1:

The patent introduces a pseudonym association service as an intermediary between first-party data holders and third-party data users. This service enables data utilization without direct access to personal information by using pseudonymized data that can be associated through a controlled matching process, thus resolving the contradiction between data accessibility and privacy protection

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent creates pseudonym copies of personal information that retain statistical and behavioral characteristics necessary for data analysis while removing direct personal identifiers. These pseudonymized copies enable third-party data utilization without exposing actual personal information, maintaining both data utility and privacy protection

Inventive Principle:

Principle #26Copying

2Measurement precision

If pseudonym association data is collected and stored for model construction, then data accuracy is improved, but data volume and processing complexity increase

Engineering Contradiction:

Improvedata estimation accuracyVSAvoiddata processing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the data processing workflow into distinct stages: pseudonymization, association, model construction, and data expansion. By dividing the complex process into manageable segments with clear interfaces, the system handles large volumes of pseudonym association data more efficiently while maintaining accuracy in third-party data estimation

Inventive Principle:

Principle #1Segmentation

3Adaptability or versatility

If third-party data is expanded and combined with first-party data, then advertising targeting capability is improved, but data privacy protection becomes more challenging

Engineering Contradiction:

Improveadvertising targeting capabilityVSAvoidprivacy exposure risk

Core Design Contradiction:

Adaptability or versatilityVSObject-affected harmful factors

Solution Approach 1:

The pseudonym association service acts as a trusted intermediary that enables the combination and expansion of first-party and third-party data for enhanced advertising targeting. The intermediary controls the association process using pseudonymized data, allowing sophisticated targeting capabilities while preventing direct exposure of personal information throughout the data expansion process

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20240144321A1Method, system, and recording medium for expanding and utilizing data using pseudonymous association

Publication Date: 2024.05.02 NAVER CORP

AI summary

Disclosed are a method, a system, and a recording medium for expanding and utilizing data using pseudonym association. The method of expanding and utilizing data using pseudonym association includes receiving pseudonym association data in which first-party information of a customer company and third-party information of an entity other than the customer company are pseudonymized and combined; generating an interpretation model for data estimation by modeling the pseudonym association data; estimating the third-party information using target information of the customer company through the interpretation model; and providing the estimated third-party information for a service related to the customer company.