Emergency Data Statistics Aggregation with Privacy Protection
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Solution Overview
Problem
Existing emergency data management systems in E911 and NG911 networks face challenges in aggregating and analyzing emergency data while maintaining privacy, as they are restricted by laws like HIPPA, preventing real-time statistical analysis and storage of protected data.
Innovation Solution
An emergency data manager with a statistics module that filters incoming data in real-time using criteria like geofence, time, and access credentials, generates data bins without storing the actual data, and provides real-time statistical reports, ensuring compliance with privacy regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If emergency data is stored for later analysis, then statistical analysis capability is improved, but data privacy protection is compromised
Solution Approach 1:
The patent extracts only the statistical characteristics and patterns from emergency data while removing all personally identifiable information (PII) and protected health information (PHI). The system processes data to extract aggregate statistics such as call volumes, response times, and resource utilization metrics without retaining any information that could identify individual patients or emergency events, thus enabling analysis while protecting privacy.
Solution Approach 2:
The patent introduces an intermediary processing layer that acts as a mediator between raw emergency data and statistical analysis. This intermediary component performs real-time aggregation and anonymization, transforming protected data into statistical insights without exposing underlying sensitive information. The intermediary ensures that analysis can proceed on aggregated statistics while the original protected data remains inaccessible and unstored.
2Productivity
If real-time statistical analysis is implemented, then operational monitoring capability is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the data processing function into distinct modular components: data reception modules that collect emergency data, filtering modules that remove PII and PHI, aggregation modules that compute statistical metrics, and output modules that generate reports. This segmentation allows each component to perform its specific function independently, reducing overall system complexity while enabling real-time statistical analysis across multiple data sources.
Solution Approach 2:
The patent performs preliminary filtering and aggregation actions as data is being received, rather than processing all data after collection. By pre-filtering out sensitive information and pre-aggregating statistics in real-time during data ingestion, the system reduces the computational burden on subsequent analysis stages and enables faster operational monitoring without requiring complex post-processing operations.
3Object-affected harmful factors
If protected data is deleted immediately, then data privacy protection is improved, but data mining capability deteriorates
Solution Approach 1:
The patent implements dynamic data retention policies where the system adaptively manages data based on processing stage and sensitivity. Raw protected data is retained temporarily in secure buffers during active emergency response operations, then automatically deleted after aggregation. The system dynamically transitions data from a retained state during processing to a deleted state after statistical extraction, optimizing both privacy protection and mining capability at different temporal stages.
Solution Approach 2:
The patent changes the state parameters of data during processing: transforming protected health information from its original detailed form into aggregated statistical parameters such as call volumes, response times, and resource utilization metrics. This parameter transformation converts sensitive individual-level data into population-level statistics, enabling data mining for operational improvements while the changed parameters no longer contain identifiable information and can be safely retained for analysis.
Data Source
AI summary
One disclosed method includes filtering incoming emergency data using at least one filtering criteria, as the emergency data is received in response to initiation of emergency events; generating at least one data bin representing a tally of emergency data meeting the at least one filtering criteria, without storing any of the emergency data used to generate the at least one data bin; and generating a statistical report using the at least one data bin. The statistical report may be generated real-time and displayed on a remote display.