Anonymous Mobile User Data Collection via Trusted Third Party
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Solution Overview
Problem
Existing methods for counting active mobile phones fail to collect statistical data about users without invading their privacy, particularly in situations where information on origin, education, and travel routes is needed without compromising user privacy.
Innovation Solution
A method that utilizes a Trusted Third Party to convert mobile phone numbers into anonymous unique numbers, retrieves postal codes from a database, and uses additional statistical data about residents per postal code to gather information, including sending unnoticeable text messages to determine housing locations for prepaid and business phones, while maintaining user privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If mobile phone numbers are stored and processed to collect statistical data about users, then statistical information about origin, education, and travel routes can be obtained, but user privacy is invaded
Solution Approach 1:
A Trusted Third Party acts as an intermediary between the data collection system and user identifiers. The TTP converts mobile phone numbers into anonymous unique numbers, enabling statistical data collection while preventing direct linkage to individual users. This mediator ensures that statistical information can be gathered without exposing personal identities or invading privacy.
Solution Approach 2:
Instead of processing original mobile phone numbers directly, the system creates and processes anonymous unique numbers that copy the essential identifying function without containing personal information. These anonymous identifiers serve as substitutes that maintain data utility for statistical purposes while eliminating privacy risks associated with real phone numbers.
2Loss of information
If postal codes are retrieved and coupled with unique numbers to gather user information, then statistical data about users can be collected, but the complexity of the system increases
Solution Approach 1:
The system merges multiple data sources (mobile phone location data, postal code information, and statistical databases) into a unified analysis framework. By combining these elements through the anonymous unique numbers, the system achieves comprehensive statistical data collection while managing complexity through integrated processing rather than separate systems.
Solution Approach 2:
The anonymous unique numbers serve multiple functions: they identify mobile phones for tracking purposes, link to postal code information, and enable statistical analysis without revealing personal identities. This multi-functionality reduces the need for separate identification systems and simplifies the overall data collection architecture.
3Loss of information
If text messages are sent to determine housing locations of prepaid and business phone users, then location data can be obtained for users without known postal codes, but additional communication overhead is introduced
Solution Approach 1:
The system performs preliminary actions by sending text messages at predetermined times when users are expected to be at home. This proactive approach establishes housing locations in advance, creating a database of location information that can be reused for future statistical analyses without requiring repeated communication with users.
Solution Approach 2:
Users indirectly participate in the location determination process by responding to text messages with their location information. The system leverages user cooperation through simple self-service actions (replying to messages) to gather location data, reducing the need for complex active tracking mechanisms.
Data Source
AI summary
In a method data are collected about users of mobile telephones which are present within a plurality of defined cells between two specified points in time. For this purpose a list of telephone numbers of mobile telephones is retrieved from a data base, which mobile telephones were located in the defined cells between the two specified points in time. The list of telephone numbers is converted into unique numbers to avoid violation of privacy. Subsequently, the cells are defined in which the housing locations of the users of these mobile telephones are situated. This is effected by sending non-noticeable text messages to mobile telephones on various days at a number of different points in time at which most people are expected to be at home. Based on these text messages the locations of the mobile telephones are established.Furthermore, the postal code areas are determined in which the cells of the housing locations are situated and, subsequently, the statistical data associated with these postal codes are retrieved from a further data base. These statistical data are then coupled to the unique numbers of the mobile telephones that were present in defined cells between the specified points in time.