Vehicle Data Obfuscation Correction via Cluster Size Feedback
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Solution Overview
Problem
Current data anonymization methods for vehicle-related data lack a means to check and correct anonymization estimations in real-time, leading to potential reductions in data quality due to over-security measures, which can compromise the informational value of anonymized data sets.
Innovation Solution
A method and device for monitoring and correcting the obfuscation of vehicle-related data using an anonymization filter that removes personal data, combines data to create an anonymous set, and adjusts obfuscation based on actual cluster size comparisons, ensuring spatial and temporal accuracy, with a correction map to adapt to real-time conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additional security measures are applied to ensure data protection compliance, then data protection reliability is improved, but data quality is worsened
Solution Approach 1:
The system implements a feedback mechanism where the actual cluster size is determined from anonymized data sets and compared with the estimated cluster size used for obfuscation. This comparison result is then used to correct the obfuscation parameters, creating a closed-loop control system that continuously improves anonymization accuracy while maintaining data quality.
Solution Approach 2:
The system dynamically adjusts obfuscation parameters based on the comparison between actual and estimated cluster sizes. By changing the obfuscation parameters (such as spatial aggregation levels or temporal smoothing factors) according to real-world data characteristics, the system optimizes the balance between data protection and data utility.
2Reliability
If obfuscation parameters are adjusted to enhance data protection, then security is improved, but measurement precision is worsened
Solution Approach 1:
The monitoring mechanism provides feedback on the effectiveness of obfuscation by comparing actual cluster sizes with estimated ones. This feedback enables continuous refinement of obfuscation parameters to achieve optimal security without excessive loss of measurement precision.
Solution Approach 2:
The system transitions from static obfuscation parameters to dynamic parameter adjustment. Obfuscation intensity is adapted in real-time based on actual data characteristics and security requirements, allowing the system to maintain high security while preserving necessary data accuracy for different应用场景.
Data Source
AI summary
Technologies and techniques for monitoring and correcting the obfuscation of vehicle-related data. Personal data may be removed from the transmitted vehicle-related data, and the vehicle-related data after the removal of the personal references may be combined to form a vehicle-related data set without personal references. A temporal and spatial obfuscation of the vehicle-related data set may be carried out without personal references based on an estimated size of a cluster of data-collecting vehicles for generating an anonymous data set. The anonymous data set, including the degree of obfuscation, may be provided to a data user and an actual cluster size for a spatial region based on the provided anonymous data sets may be determined and compared with the estimated cluster size used for the obfuscation of the anonymous data set. The comparison result may be then used to correct the obfuscation of the spatial region.