Mobile Alias Identification via Mobility Profile Analysis
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Solution Overview
Problem
Current systems fail to effectively identify whether multiple mobile units belong to the same user, particularly in cases where users employ multiple phones with different identifiers or replace phones frequently, making it difficult to track user mobility and detect potential aliases.
Innovation Solution
The system analyzes historical location data and calling patterns to determine if mobile units are used by the same person, utilizing mobility profiles and a matching profile algorithm to identify spatial and temporal transitions, and correlates location data with time zones to determine if phones belong to the same user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If users employ multiple mobile phones with different identifiers or replace phones frequently, then user anonymity and avoidance of tracking is improved, but the ability to identify whether mobiles belong to the same user deteriorates
Solution Approach 1:
The system continuously monitors and analyzes mobile behavior patterns, location data, and calling patterns over time, using feedback loops to refine identification accuracy. The system compares observed behaviors against established profiles to dynamically adjust identification confidence levels and update user profiles with new behavioral data.
Solution Approach 2:
The system introduces behavioral pattern analysis and mobility profiles as intermediary layers between mobile identifiers and user identity. Instead of directly linking mobile IDs to users, the system uses intermediate behavioral characteristics (location patterns, calling habits, device usage patterns) to infer relationships, thereby identifying users without requiring direct identifier tracking.
2Measurement precision
If the system analyzes historical location data and calling patterns to identify mobile aliases, then mobile alias detection capability is improved, but system complexity and data processing requirements worsen
Solution Approach 1:
The system segments the complex identification problem into distinct analytical modules: location pattern analysis, calling pattern analysis, device usage analysis, and temporal behavior analysis. Each module processes specific types of data independently and generates separate findings that are then integrated, making the overall system more manageable and efficient.
Solution Approach 2:
The system implements progressive analysis where it first identifies obvious aliases using simple criteria, then applies more complex analysis only to ambiguous cases. The matching profile algorithm uses threshold-based filtering to process large datasets efficiently, applying full analytical depth only where necessary to resolve uncertain identifications.
3Reliability
If the system tracks mobile units over extended periods to build mobility profiles, then identification reliability is improved, but time consumption and data storage requirements worsen
Solution Approach 1:
The system performs preliminary analysis of mobile behavior patterns early in the tracking period to establish baseline mobility profiles. Once sufficient data is collected to form reliable patterns, the system can make preliminary identification decisions without requiring the full tracking period, reducing overall time consumption while maintaining reliability.
Solution Approach 2:
The system uses temporary behavioral indicators and short-term pattern matching to quickly identify obvious aliases without requiring long-term tracking. For cases where quick identification is sufficient, the system accepts shorter observation periods, effectively using 'disposable' short-term data analysis rather than always requiring extensive long-term profiles.
Data Source
AI summary
A system and method according to the principles of the invention identifies mobile phone aliases. The system processes mobile location data and call event data to generate mobility profiles. The profiles indicate a mobile's geographic zone history over a specified time. To produce a mobility profile, the system aggregates location data into zones and associates the zones with times of day, week or month. Particular zones for different mobiles can be compared according to weighting algorithms to provide data indicating whether the mobiles belong to the same user.