Deterministic Identifier Correlation for Privacy-Preserving Demographic Analysis
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Solution Overview
Problem
Current methods for analyzing user demographics across different media platforms often rely on personally identifiable information (PII), which raises privacy concerns and compliance challenges due to the volume of data and diverse regulatory environments.
Innovation Solution
A computer-implemented method that correlates user information from online and offline content access using deterministic, unique identifiers created from IP addresses, without sharing personal identifiable information, allowing for anonymous data aggregation and analysis across multiple media platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If personally identifiable information (PII) is used to analyze user demographics across different media platforms, then the accuracy and completeness of demographic profiles can be improved, but user privacy protection deteriorates and compliance with privacy regulations becomes difficult
Solution Approach 1:
The patent extracts and removes personally identifiable information (PII) from user data before analysis. Deterministic identifiers are created from IP addresses through hashing and obfuscation, separating the identification function from the actual PII. This allows demographic analysis to proceed using anonymized data that cannot be traced back to individual users, thus maintaining profile accuracy while protecting privacy.
Solution Approach 2:
The patent introduces deterministic identifiers as an intermediary between PII and analysis systems. These identifiers are derived from IP addresses through deterministic hashing, creating a middle layer that enables user tracking and demographic profiling without exposing actual PII. The intermediary maintains the functional capability of user identification while eliminating privacy risks associated with direct PII exposure.
2Loss of information
If PII is shared with third-party matching services to create matched data sets, then the completeness of user profiles can be improved, but the complexity of data management and compliance verification increases
Solution Approach 1:
The patent performs preliminary anonymization of user data by creating deterministic identifiers from IP addresses before data is shared with third-party matching services. This advance processing ensures that PII is never exposed during data matching operations, eliminating the need for complex compliance verification and data management protocols while still enabling complete user profile construction through anonymized data correlation.
3Measurement precision
If traditional data matching methods are used to correlate online and offline content access, then the accuracy of cross-platform analysis can be improved, but the processing time and computational resources increase significantly
Solution Approach 1:
The patent replaces traditional mechanical data matching processes with deterministic identifier-based correlation. Instead of comparing full PII datasets across platforms, the system uses hashed and obfuscated identifiers derived from IP addresses to quickly match user activity across online and offline content. This substitution dramatically reduces computational complexity and processing time while maintaining the accuracy of cross-platform user behavior analysis.
Data Source
AI summary
Providing analysis of exposure of users to content without the use of personally identifiable information (PII) is described. Initially, first user activity data is obtained from a server associated with a collection service. The first user activity data comprises a first unique identifier (ID) deterministically created based on an Internet protocol (IP) address of a first entity using a first one-way function. Next, second user activity data is obtained from a service provider. The second user activity data comprises a second unique ID deterministically created based on PII of a second entity using a second one-way function. It is determined whether the first unique ID is the same as the second unique ID, and responsive to that determination, the first and second user activity data are continuously aggregated such that a trend is determined in real-time and analysis of the aggregated user activity data is performed and reported in compliance with mandated legal or policy privacy provisions.