Anonymous Entity Mapping With Confidence Scoring for Account Matching
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Solution Overview
Problem
Internet users remain anonymous, making it difficult for website operators to initiate contact or marketing efforts, as they lack sufficient identifying information beyond IP addresses and cookies.
Innovation Solution
A system and method to map anonymous entities such as IP addresses, domains, and cookies to known accounts, using a platform that aggregates and normalizes data to determine a confidence value for associating these entities with specific companies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If website operators collect and analyze visitor data, then they can identify and contact visitors for marketing purposes, but visitor anonymity is compromised
Solution Approach 1:
The patent employs an intermediary mapping system that connects anonymous entities (IP addresses, devices) to known accounts through probabilistic matching algorithms. This intermediary layer allows operators to identify visitors without directly exposing or collecting personally identifiable information, thus resolving the contradiction between gaining visitor identification and maintaining anonymity.
Solution Approach 2:
The system implements feedback loops where mapping results are continuously refined based on confidence scores and validation against multiple data sources. This feedback mechanism enables the system to improve identification accuracy over time while maintaining privacy protections, allowing operators to contact visitors with higher reliability without compromising anonymity principles.
2Ease of operation
If operators use only IP addresses and cookies to identify visitors, then visitor privacy is maintained, but sufficient identifying information is unavailable for marketing contact
Solution Approach 1:
The patent merges multiple data sources including IP addresses, device identifiers, browsing behavior patterns, and external data sets into a unified mapping framework. By combining these diverse information types through probabilistic algorithms, the system generates confidence scores that enable visitor identification and contact capability while maintaining privacy through the use of aggregated rather than individual data points.
3Measurement precision
If the mapping system aggregates data from multiple sources, then identification accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the complex mapping system into distinct modular components: data collection modules, normalization modules, matching algorithm modules, and confidence scoring modules. Each module handles a specific aspect of the data aggregation process, making the overall system more manageable and maintainable while achieving high identification accuracy through the coordinated operation of these specialized components.
Data Source
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AI summary
The invention sets out a method for de-anonymizing anonymous online activities, the method comprising using at least one hardware processor to: receive a plurality of events from a plurality of sources, wherein each of the plurality of events represents an online activity and indicates an entity; aggregate subsets of the plurality of events into a plurality of mappings, wherein each of the plurality of mappings associates the entity, shared by a subset of the plurality of events, with an account, and is associated with one or more statistics regarding the subset of events; for each of the plurality of mappings, compute a confidence value for the mapping based on the associated one or more statistics; select a final subset of the plurality of mappings based on the confidence values for the plurality of mappings; and respond to one or more requests that indicate a requested entity by searching the final subset of mappings for the requested entity, and returning an indication of an account associated with the requested entity in the final subset of mappings. A corresponding system and non-transitory computer-readable medium are also disclosed.