Matching Anonymized User Identifiers via Geolocation and Time
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Solution Overview
Problem
Existing user profiling methods face challenges in matching anonymized user identifiers across differently anonymized data sets, making it difficult to create comprehensive user profiles due to unique identifiers used by various data providers and the complexity of updating profiles when users change devices.
Innovation Solution
A process involving obtaining location histories from mobile devices, determining location-attribute scores, and matching user identifiers based on geolocations and times to create user profiles that are stored in a datastore, allowing for the generation of user profiles that are privacy-friendly and accurate by inferring intermediate locations and accounting for time-based attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user identifiers are anonymized by different data providers, then user privacy is protected, but the ability to match user records across data providers deteriorates
Solution Approach 1:
The patent introduces an intermediary matching process that uses non-identifier attributes (location, time, device characteristics) as mediators to connect anonymized user records from different providers. Instead of directly matching identifiers, the system uses these intermediary attributes to establish correlations between users across data providers, thereby protecting privacy while enabling matching.
Solution Approach 2:
The patent transforms the matching problem from identifier-based to attribute-based by changing the parameters used for matching. Instead of relying on unique identifiers, the system uses location coordinates, timestamps, and device parameters as matching criteria, fundamentally changing the approach to user identification across anonymized datasets.
2Quantity of substance
If data from multiple sources is aggregated, then user profile comprehensiveness is improved, but data matching difficulty increases
Solution Approach 1:
The patent segments the matching process into multiple independent stages: first matching location attributes, then time attributes, and finally device characteristics. This segmentation breaks down the complex multi-attribute matching problem into smaller, more manageable sub-problems, making it feasible to process large volumes of data from multiple sources.
Solution Approach 2:
The patent adds temporal and spatial dimensions to the matching process by incorporating time stamps and location coordinates as matching criteria. This multi-dimensional approach allows the system to match users across data providers by comparing their movement patterns and temporal behavior, transforming a difficult single-dimension matching problem into a more solvable multi-dimensional problem.
3Measurement precision
If users manually input their data, then profile accuracy is improved, but user effort and time increase
Solution Approach 1:
The patent implements self-service by enabling the system to automatically collect and process user data from mobile devices without requiring manual user input. The system extracts location, time, and device information directly from device sensors and logs, allowing users to benefit from accurate profiling while the system handles all data collection and processing automatically.
Solution Approach 2:
The patent replaces the mechanical process of manual data entry with automated electronic data extraction from device sensors and logs. Instead of users manually typing or inputting information, the system automatically captures location data from GPS, timestamps from system logs, and device characteristics from hardware identifiers, eliminating the need for user effort while maintaining high accuracy.
4Reliability
If user profiles are updated frequently, then profile currentness is improved, but processing complexity increases
Solution Approach 1:
The patent implements periodic action by updating user profiles at scheduled intervals based on accumulated data rather than continuously processing every new data point. The system aggregates location and behavioral data over time periods and performs batch updates, reducing processing complexity while maintaining profile currentness through regular periodic refreshes.
Data Source
AI summary
Provided is a process of obtaining a plurality of location data sets from different providers of user geolocation history, each location data set including a plurality of user-activity records, each user-activity records being associated with a user identifier and including geolocations of the corresponding user and times that the corresponding user was at the geolocations, the different providers having different user identifiers for a given corresponding user; matching, by one or more processors, the user identifiers between the location data sets based on geolocations of the corresponding user and times that the corresponding user was at the geolocations; and storing the matched user identifiers in association with one another in corresponding user profiles.