IoT Data Aggregation via Enterprise Ontology and Pseudonymization
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Solution Overview
Problem
Current tools and techniques for extracting and securely ingesting member, constituent, or patient data from connected devices lack comprehensive solutions for protecting identifiable data without exposing it to security threats and fail to dynamically map contents from various devices to enterprise ontology, providing semantic linkage.
Innovation Solution
A system that collects, analyzes, and visualizes data from disparate sources, securely ingests data into a persistent database, decouples and pseudonymizes identifiable data, and maps semantics to enterprise ontological schema, using algorithms for de-identification and semantic mapping to integrate and associate information from IoT devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is securely ingested and pseudonymized to protect privacy, then data protection is improved, but data accessibility and usability deteriorate
Solution Approach 1:
The patent introduces an enterprise ontology as an intermediary layer between raw pseudonymized data and user queries. The ontology provides semantic meaning and structure to the data without exposing identifying information, allowing users to access and analyze data meaningfully while maintaining privacy protection through the abstraction layer.
Solution Approach 2:
The system transforms data from its original form through pseudonymization (changing identifiers) and semantic mapping (changing representation format). By altering the parameters of data representation while preserving underlying relationships, the system maintains both protection and accessibility - data is protected through parameter transformation but remains queryable through the ontology interface.
2Quantity of substance
If data from multiple disparate devices is aggregated and integrated, then data completeness is improved, but system complexity increases
Solution Approach 1:
The enterprise ontology serves as a universal framework that can accommodate data from multiple different device types and formats. By defining a common semantic structure that multiple data sources can map to, the system achieves data aggregation without proportionally increasing complexity - the ontology handles the variation in device formats through standardized mapping relationships.
Solution Approach 2:
The patent segments the complex task of multi-device data integration into distinct components: data collection from various devices, pseudonymization processing, semantic mapping to ontology, and storage. By dividing the integration process into separate manageable stages, each handling a specific aspect of the complexity, the system can aggregate comprehensive data while keeping overall system complexity controlled through modular architecture.
3Object-affected harmful factors
If identifiable data is decoupled and pseudonymized, then privacy security is improved, but data processing and mapping complexity increases
Solution Approach 1:
The system performs pseudonymization as a preliminary action during data ingestion, before data is stored or processed further. By removing identifiable information upfront in the data collection stage rather than attempting to anonymize later, the system achieves privacy security while minimizing processing complexity - the difficult pseudonymization work is done once during ingestion rather than repeatedly during subsequent operations.
Solution Approach 2:
The enterprise ontology acts as an intermediary that simplifies the complexity of processing pseudonymized data. Instead of directly processing raw pseudonymized data which would be complex and meaningless, the ontology provides a structured semantic layer that makes the data accessible and processable while maintaining the privacy benefits of pseudonymization.
Data Source
AI summary
Embodiments of the disclosure provide a method for aggregating and providing health data records to an electronic device. The method is performed by a server that includes a processor and a non-transitory computer readable medium with processor-executable instructions stored thereon. When the instructions are executed by the processor, the server performs the method including: (a) receiving collected data from one or more client devices, the collected data comprising health related data including at least one of step count data, heart rate data, sleep sensor data; (b) extracting metadata from the collected data; (c) pseudonymizing the collected data; (d) categorizing the collected data using the extracted metadata and enterprise ontology of the server; and (e) storing the collected data.