Dynamic Spatial Anonymization for Vehicle Data Privacy
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Solution Overview
Problem
Current spatial anonymization techniques in cloud environments for vehicle data lack dynamic adjustment based on traffic density, leading to inaccurate data granularity, neglecting sparsely populated areas, and struggling with big data loads, resulting in less reliable and less accurate anonymization.
Innovation Solution
A method for dynamic spatial anonymization using geospatial indexing to partition vehicle data into subsets of varying sizes and locations, allowing for two-level aggregation and modification of data sets to reduce overlapping, enhancing privacy and data quality, and optimizing computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static grid structures with predefined grid sizes are used for spatial anonymization, then the anonymization process is simple to implement, but the data granularity accuracy deteriorates and sparsely populated areas are neglected
Solution Approach 1:
The patent implements dynamic spatial anonymization by replacing static grid structures with a dynamic clustering approach. The system continuously adjusts cluster boundaries and sizes based on real-time traffic density measurements, allowing the anonymization grid to adapt its resolution dynamically. In high-density areas, finer granularity is applied while sparsely populated areas use coarser aggregation, resolving the contradiction between implementation simplicity and data accuracy.
Solution Approach 2:
The system changes the parameter of grid resolution dynamically based on traffic density conditions. By monitoring traffic density and adjusting the clustering parameters accordingly, the system maintains high data granularity accuracy in dense areas while using coarser aggregation in sparse areas, thus resolving the contradiction between ease of implementation and measurement precision.
2Device complexity
If predefined grid sizes are used for spatial anonymization, then the system structure is simple, but the ability to handle sparsely populated areas deteriorates
Solution Approach 1:
The dynamic clustering system automatically adjusts to handle sparsely populated areas by forming smaller, more precise clusters where needed. The system monitors traffic density and dynamically creates or merges clusters accordingly, ensuring reliable anonymization in both dense and sparse areas without requiring complex predefined structures.
Solution Approach 2:
The system changes clustering parameters such as minimum cluster size and density thresholds to adapt to different geographic conditions. This allows the system to maintain reliable anonymization in sparsely populated areas by adjusting parameters to accommodate low-density conditions, while keeping the overall system structure relatively simple.
3Ease of manufacture
If current spatial anonymization techniques are used with big data loads, then the processing approach is straightforward, but the computational efficiency and runtime deteriorate
Solution Approach 1:
The patent segments the large dataset into smaller sub-datasets based on spatial clusters and traffic density zones. By dividing the big data load into manageable chunks that can be processed in parallel, the system significantly improves computational efficiency and reduces runtime while maintaining straightforward processing logic for each segment.
Solution Approach 2:
The dynamic clustering approach allows the system to adapt the processing scope based on data distribution. By dynamically adjusting cluster boundaries and processing only relevant areas, the system avoids unnecessary computations across the entire dataset, thus improving productivity while keeping the processing approach relatively simple.
4Ease of manufacture
If static grid anonymization is applied, then the method is easy to implement, but the adaptability to traffic density variations deteriorates
Solution Approach 1:
The system implements dynamic adaptability by continuously monitoring traffic density and adjusting cluster configurations in real-time. This dynamic approach allows the anonymization method to adapt to varying traffic conditions without requiring complex reimplementation, thus resolving the contradiction between implementation ease and adaptability.
Solution Approach 2:
The system incorporates feedback mechanisms where traffic density measurements are used to adjust anonymization parameters. This feedback loop enables the system to adapt to traffic density variations automatically, maintaining ease of implementation while significantly improving adaptability through continuous parameter adjustment based on real-time conditions.
Data Source
AI summary
The disclosure relates to a method for dynamic spatial anonymization of vehicle data in a cloud environment. The method may comprise: collecting vehicle data, spatial partitioning the vehicle data into data subset associated with different geographical areas of various sizes and comprising a maximal amount of records within each data subset, spatial aggregation of the vehicle data within the data subsets, providing two level aggregation, namely: aggregation of vehicle data, coming from a single vehicle, to a corresponding data point and aggregation data points, coming from a group of vehicles comprising a particular number of vehicles, to a spatial aggregated data set.