Incentivizer System for Cross-Domain Identifier Binding
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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
An incentivizer system that uses brokers to encourage entities to perform specific behaviors across multiple domains, allowing for the binding of identifiers and correlation of behavior data without compromising privacy, by rewarding operators and providing statistical information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cross-domain identifier binding is implemented to correlate behavior data, then measurement precision of cross-domain behavior is improved, but consumer privacy is compromised
Solution Approach 1:
The patent introduces a broker as an intermediary component that mediates between domain operators and the binder. The broker receives data from operators, processes it to identify cross-domain bindings, and shares only the necessary binding information with the binder without exposing raw consumer data. This intermediary structure enables cross-domain correlation while maintaining consumer privacy by preventing direct access to identifiable consumer information across domains.
Solution Approach 2:
The system segments the data processing function into separate components: domain operators collect data within their domains, the broker handles cross-domain binding identification, and the binder performs the actual correlation. This segmentation allows each component to operate with limited access to data, preventing any single entity from having complete consumer profiles while still enabling cross-domain analysis.
2Productivity
If domain owners share information across domains, then productivity of cross-domain analysis is improved, but loss of information confidentiality occurs
Solution Approach 1:
The broker serves as a trusted intermediary that domain owners can share information with without directly exposing their data to external entities. The broker processes and binds identifiers across domains while maintaining confidentiality, allowing domain owners to benefit from cross-domain analysis productivity while preserving their data's confidentiality through the broker's mediation.
Solution Approach 2:
The system extracts only the necessary binding information (linkage between identifiers across domains) from the full dataset and shares this extracted information with the binder. This extraction approach enables cross-domain analysis productivity by providing the binder with sufficient information to correlate behaviors while preventing loss of overall data confidentiality by not sharing the complete raw datasets.
3Measurement precision
If identifier binding across domains is performed, then measurement precision of consumer behavior is improved, but device complexity increases
Solution Approach 1:
The complex identifier binding task is segmented into distinct functional components: domain operators collect and store identifiers within their domains, the broker handles the complex cross-domain binding logic, and the binder performs the final correlation analysis. This segmentation distributes system complexity across multiple specialized components rather than concentrating it in a single complex system, making the overall architecture more manageable while achieving precise cross-domain behavior measurement.
Solution Approach 2:
The broker acts as a specialized intermediary that handles the complex computational task of cross-domain identifier binding. By isolating this complex functionality in a dedicated component, the system achieves precise consumer behavior correlation without requiring every domain operator or endpoint to implement the full complexity of cross-domain binding logic, thus reducing overall system complexity.
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.