Personal information de-identification processing and analysis system
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Solution Overview
Problem
Modern systems integrating sensor data face challenges in handling personal information exposure risks while maintaining effective information collection and analysis, necessitating a method for de-identification processing that considers data modality, environment, time, and location.
Innovation Solution
A personal information de-identification processing and analysis system utilizing a vector database and knowledge graph to convert sensor data into vector format, perform similarity searches, and integrate context information for accurate analysis while protecting personal information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data including personal information is collected and analyzed to improve situation understanding, then analysis accuracy is improved, but personal information exposure risk increases
Solution Approach 1:
The patent introduces a de-identification processing module as an intermediary between sensor data collection and analysis. This module transforms personal information-containing data into anonymized data before analysis, allowing accurate situation understanding while preventing direct exposure of personal information. The intermediary layer enables the system to maintain analysis accuracy without compromising privacy.
Solution Approach 2:
The patent extracts and removes personal information from sensor data through de-identification processing. By separating and removing identifiable personal details while retaining the essential patterns and relationships needed for analysis, the system achieves accurate situation understanding without exposing personal information. This extraction approach allows the core analytical value to be preserved while eliminating privacy risks.
2Object-affected harmful factors
If de-identification processing is performed on sensor data to reduce personal information exposure, then privacy protection is improved, but data analysis accuracy may deteriorate
Solution Approach 1:
The patent applies parameter changes by transforming data characteristics through de-identification processing. The system modifies data parameters (such as anonymizing identifiers while preserving temporal and spatial patterns) to maintain analytical value. By carefully adjusting these parameters, the system achieves privacy protection without significant loss of analysis accuracy, as the transformed data retains essential relationships needed for situation understanding.
3Measurement precision
If multiple sensor data are integrated and analyzed to understand complex situations, then situation understanding is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the data processing workflow into distinct modules: sensor data collection, de-identification processing, and analysis. This segmentation allows each module to handle specific tasks independently, reducing overall processing complexity. The de-identification module processes personal information separately from the analysis module, enabling modular and manageable complexity while maintaining comprehensive situation understanding through integrated data processing.
Data Source
AI summary
A personal information de-identification processing and analysis system for analyzing sensor data including personal information after performing de-identification processing on the sensor data is provided. To this end, the personal information de-identification processing and analysis system includes a sensor module configured to collect at least two pieces of sensor data, a vector conversion module configured to convert the sensor data collected by the sensor module into vector data, a vector DB configured to store the vector data, a vector search module configured to search the vector DB for vector data most similar to the vector data converted by the vector conversion module, and an output module configured to output the searched vector data.