Private-Identifier Matching for Non-Identifiable User Association
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Solution Overview
Problem
Existing methods for associating anonymous information with personally identifiable information (NPII) risk violating privacy regulations and deliver incorrect advertisements by obfuscating PII with noise or grouping users broadly, failing to target specific audiences effectively.
Innovation Solution
A technique that generates groups of users with shared characteristics while obfuscating PII by using private IDs and rule-based heuristics to reject-out users with mutually-exclusive attributes and select-in those with similar traits, ensuring anonymity and targeted advertising.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If legacy techniques add noise to obfuscate PII pairing, then privacy is protected, but incorrect information is associated with user profiles
Solution Approach 1:
The patent introduces a private identifier as an intermediary element that bridges PII and NPII datasets without exposing actual PII. This mediator enables accurate association of user information across datasets while maintaining privacy protection, avoiding both noise addition and broad aggregation.
Solution Approach 2:
The patent creates a copy of the PII dataset with private identifiers instead of using actual PII. This copied dataset maintains the structural relationships needed for accurate user profiling while eliminating privacy risks associated with exposing real personal information.
2Reliability
If legacy techniques aggregate multiple users into groups to obfuscate PII, then privacy is protected, but user groups become overly broad and targeting precision is lost
Solution Approach 1:
Private identifiers serve as mediators that enable precise matching between PII and NPII datasets without requiring broad user aggregation. This allows accurate targeted advertising to individual users while maintaining privacy, eliminating the need for overly broad groupings.
Solution Approach 2:
The patent changes the identification parameter from direct PII to private identifiers. This parameter transformation enables both precise user targeting and privacy protection simultaneously, avoiding the trade-off between grouping precision and privacy that plagues legacy approaches.
3Productivity
If advertisers collect detailed PII for targeted advertising, then advertising effectiveness is improved, but privacy regulations are violated
Solution Approach 1:
The patent creates a copied version of the PII dataset containing only private identifiers and necessary matching keys. This copy enables effective targeted advertising by maintaining user identification and preference data while eliminating actual PII that would trigger privacy regulation violations.
Solution Approach 2:
The patent extracts only the essential identification elements from PII datasets, creating minimal viable datasets with private identifiers. This extraction maintains advertising effectiveness by preserving user matching capability while removing problematic PII elements that violate privacy regulations.
Data Source
AI summary
The present disclosure provides a detailed description of techniques used in methods, systems, and computer program products for associating anonymous information with personally identifiable information without sharing any personally identifiable information. A method receives a specification record comprising one or more specified demographic attributes to be used in user record selection operations, the results of which operations include user records that comprise a user identifier and at least some non-personally-identifiable information. A candidate group is formed by applying a set of rules over the retrieved user records to selectively exclude one or more user records that comprise mutually-exclusive characteristics with respect to the other user records in the candidate group. An anonymity measure is calculated over the candidate group to satisfy a threshold of anonymity. If needed to satisfy the threshold of anonymity, additional user records are added to the group before any sharing operations. Anonymity of the users is preserved.