Anonymized Location Data Correlation for Emergency Alerting
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Solution Overview
Problem
Existing location-based systems face challenges in predicting future locations of individuals for pre-emptive alerting, especially in emergency scenarios, due to privacy concerns and limitations in using personally identifiable information, despite improved location accuracy and reduced power consumption.
Innovation Solution
A system that collects and anonymizes mobile device user data, using location-based services and geo-fencing to identify users who have been in proximity to events of interest, allowing for notification of subscribers without revealing identifying information, through a remotely-executable correlation model and API layer for data retrieval and obfuscation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If personally identifiable information is used for location-based alerting, then alerting effectiveness is improved, but user privacy is compromised
Solution Approach 1:
The patent extracts personally identifiable information (PII) from the location data system. By removing identifying elements such as device IDs, phone numbers, and names from location datasets, the system maintains alerting effectiveness through anonymized location matching while eliminating privacy risks associated with storing and processing sensitive personal information.
Solution Approach 2:
The patent introduces anonymized location data as an intermediary between users and the alerting system. Instead of directly using PII for location matching, the system employs anonymized datasets that serve as a mediator, enabling contact tracing and alerting functionality while preventing direct exposure of personal information to system operators or third parties.
2Measurement precision
If detailed location data is collected for accurate tracking, then contact tracing accuracy is improved, but data privacy risks increase
Solution Approach 1:
The patent extracts identifying information from location datasets while retaining precise location and temporal data. By removing PII such as device identifiers and user names but maintaining accurate location coordinates and timestamps, the system achieves high contact tracing precision without the privacy risks associated with storing detailed personal information.
Solution Approach 2:
The patent changes the parameter state of location data by transforming identifiable information into anonymized form. Through techniques such as hashing, aggregation, and generalization, the system modifies data parameters to remove personal identifiers while preserving the spatial and temporal precision necessary for accurate contact tracing and proximity detection.
3Speed
If real-time location monitoring is implemented, then emergency response speed is improved, but power consumption increases
Solution Approach 1:
The patent implements periodic location reporting instead of continuous real-time monitoring. Mobile devices report location data at scheduled intervals or when entering predefined geo-fenced areas, rather than continuously transmitting position information. This periodic approach enables timely emergency response through event-triggered alerts while significantly reducing the power consumption associated with constant GPS and network activity.
Solution Approach 2:
The patent establishes predefined geo-fenced areas and alerting rules in advance. When a device enters or exits these pre-configured zones, automated alerts are triggered without requiring continuous monitoring or real-time user input. This preliminary setup enables rapid emergency response through pre-established detection parameters while minimizing ongoing power consumption by activating location services only when relevant events occur.
Data Source
AI summary
Systems and methods are provided for correlating a person/event of interest with other persons based on mobile device usage/location. Personally identifiable information can be kept hidden/obfuscated to protect user privacy in the case of a person of interest, as well as persons correlated to that person/event of interest. Information can be anonymized and posted for sharing with mobile device service providers, such as cellular carriers. A remotely-executed and customizable correlation engine can identify those cellular subscribers that were near/in the same location as the user. A notification alert can be sent, e.g., via an Amber Alert-like system to cellular subscribers that have been in proximity to the user or known areas in which events-of-interest have occurred. Location-based datasets can be flattened into an optimized data structure reflecting preferred location logics, and an application programming interface (API) and obfuscation layer can be used based on the flattened datasets.