Cross-Domain Identifier Binding via Stimulus-Response Mediation
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Solution Overview
Problem
The proliferation of subscription services and online platforms leads to fragmented consumer data, making it difficult to correlate cross-domain behavior, which is essential for advertisers and businesses, while also raising privacy concerns for consumers.
Innovation Solution
A method to bind identifiers across multiple information domains by creating a stimulus that elicits a response in one domain, allowing the correlation of behavior across domains without revealing personal information, using a cross-domain identifier that provides aggregated or statistical insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If identifiers are bound across multiple information domains to correlate consumer behavior, then businesses can assess advertising effectiveness and gather market intelligence, but consumer privacy is compromised and personal information is exposed
Solution Approach 1:
The patent introduces a trusted intermediary system that acts as a mediator between information domains. This intermediary binds identifiers across domains without allowing direct access to personal information, enabling cross-domain behavior correlation while maintaining consumer privacy through controlled information sharing mechanisms
Solution Approach 2:
The patent segments the identifier binding process into multiple controlled stages. Personal identifiers are separated from behavior data, with binding occurring through encrypted tokens or pseudonymous identifiers. This segmentation allows correlation of behavior patterns while preventing direct exposure of personal information
2Loss of information
If domain owners share information to enable cross-domain correlation, then advertising effectiveness can be measured, but domain owners risk losing customer trust and future business
Solution Approach 1:
The patent implements feedback mechanisms where domain owners receive controlled information about cross-domain behavior patterns without exposing individual personal data. This allows them to measure advertising effectiveness while maintaining customer trust through privacy-preserving information sharing
Solution Approach 2:
The trusted intermediary system enables domain owners to share information safely by acting as a privacy-preserving mediator. Domain owners can participate in cross-domain correlation without directly sharing sensitive customer information, thus maintaining customer trust while enabling effective measurement
3Measurement precision
If detailed personal information is collected across domains for precise behavior correlation, then advertising ROI can be accurately measured, but the complexity of data management and security increases
Solution Approach 1:
The patent extracts only the necessary identifier elements needed for correlation while leaving detailed personal information in secure domain-specific repositories. This extraction approach enables precise advertising measurement without requiring complex centralized storage and management of all personal data
Solution Approach 2:
The patent transforms personal identifiers into pseudonymous tokens or hashed values for cross-domain correlation. This parameter change maintains measurement precision by preserving unique identification capability while simplifying data management and reducing security complexity through standardized privacy-preserving transformations
Data Source
AI summary
Disclosed are methods for extracting and using information about an entity that has a presence in a number of information domains. The entity has separate identifiers in each of several domains. Various techniques are described that bind together the identifiers of the entity across the domains. The results of the binding are provided to an interested party that can review information extracted about the entity's behavior in the multiple domains. The interested party is not given access to information that would compromise the confidentiality of the entity. A trusted broker has access to information about the behavior of the entity in the several domains. The broker analyzes that information and provides the analysis to the interested party, again without compromising the confidentiality of the entity. An “incentivizer” works with the broker to extract from the domains information that would be useful in binding together the different identifiers of the entity.