Entity-to-Account Mapping for De-Anonymizing Online Activity
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Solution Overview
Problem
The challenge of maintaining anonymity on the Internet prevents website operators from effectively engaging with visitors, limiting marketing opportunities.
Innovation Solution
A method using a generalized linear model to map anonymous Internet entities like IP addresses, domains, and cookies to known accounts by generating summary mappings, applying a signal strength computation, and selecting winning mappings based on credibility and time decay, while filtering and storing these mappings for downstream use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If Internet users maintain anonymity through IP addresses, domains, and cookies, then user privacy is protected, but marketing effectiveness is limited due to inability to identify visitors
Solution Approach 1:
The patent introduces an intermediary mapping system that connects anonymous Internet entities (IP addresses, domains, cookies) to known account identifiers through summary mappings and signal strength computations. This intermediary layer enables marketing effectiveness while preserving the anonymity infrastructure, as the system maps between anonymous entities and known accounts without requiring direct disclosure of user identity information.
2Measurement precision
If multiple activity sources provide data for mapping, then mapping accuracy improves, but system complexity increases due to need to process and weigh multiple data sources
Solution Approach 1:
The patent merges multiple activity sources (form fills, cookies, email, social media) into a unified mapping framework using summary mappings that aggregate data across sources. The signal strength computation combines evidence from different sources by weighting their contributions, creating a unified accuracy metric that accounts for multiple data sources without requiring separate processing pipelines for each source.
Solution Approach 2:
The patent transforms the complexity of processing multiple activity sources into a parameter-based approach using signal strength computations. Instead of treating each activity source separately, the system computes a single signal strength parameter that aggregates the evidence from all sources, converting complex multi-source processing into a manageable parameter optimization problem.
3Manufacturing precision
If the system processes data in multiple iterations to refine mappings, then mapping quality improves, but processing time increases
Solution Approach 1:
The patent implements feedback through iterative processing where summary mappings are generated, signal strengths are computed, and winning mappings are selected in multiple iterations. Each iteration refines the mapping quality by using feedback from previous iterations to improve subsequent mappings, gradually converging on high-quality results through iterative refinement.
Data Source
AI summary
The Internet generally provides anonymity to the online activities of visitors to websites and other online resources. This prevents the operators of websites and others from identifying visitors who do not wish to be identified. Accordingly, embodiments generate mappings between entities (e.g., IP addresses, domains, cookies, or devices) and accounts (e.g., companies) to de-anonymize online activities. In an embodiment, summary mappings are generated based on activity data. Each summary mapping may comprise an entity, potential account identifier, and an activity vector that measures observations of an association between the entity and potential account identifier from an activity source for multiple summary periods. A model may be applied to the summary mappings to compute signal strengths for a plurality of candidate mappings. A winning mapping may then be selected for each entity in the candidate mappings, and used to associate the entity with an account in one or more downstream functions.