Vehicle Route Data Obfuscation With Random Segment Gaps
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Solution Overview
Problem
Current data anonymization methods for motor vehicles face challenges in ensuring complete anonymity and efficient data segmentation, leading to potential identification of vehicles and users, which complicates the collection and use of data for autonomous driving development and data protection compliance.
Innovation Solution
A procedure that subdivides data into segments with randomly chosen gap lengths and applies a local covering by shifting segment start points, combined with a time covering, to enhance anonymity and reduce the likelihood of segment recombination, thereby maintaining small gaps without increasing the anonymization group size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If data is segmented with fixed gap lengths for anonymization, then the anonymization process is simple and deterministic, but segments can be easily recombined and vehicles may be identified
Solution Approach 1:
The patent applies dynamics by making the gap lengths between data segments variable rather than fixed. The system randomly selects gap lengths from a predefined range, making the segmentation pattern dynamic and unpredictable. This prevents easy recombination of segments while maintaining a systematic anonymization process, thereby resolving the contradiction between process simplicity and anonymity assurance.
2Reliability
If larger anonymization groups are used to ensure anonymity, then vehicle identification risk is reduced, but data collection efficiency and processing complexity increase
Solution Approach 1:
The patent applies segmentation by dividing the continuous vehicle data into multiple separate segments with random gaps between them. This segmentation prevents the reconstruction of complete vehicle trajectories even with larger groups, as the random gap patterns make recombination difficult. This allows for maintaining anonymity with more efficient data collection processes rather than requiring excessively large anonymization groups.
3Quantity of substance
If more data is collected for autonomous driving validation, then algorithm training and validation improve, but data protection compliance becomes more difficult
Solution Approach 1:
The patent applies the extraction principle by removing identifying information through the segmentation process. By taking out continuous trajectory data and breaking it into segments with random gaps, the system extracts only the necessary anonymized portions for validation while eliminating the harmful identifying elements. This enables collection of sufficient data for autonomous driving validation while maintaining data protection compliance.
Data Source
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AI summary
The invention relates to a method, to a computer program with instructions, and to a device for processing data detected by a motor vehicle. The invention additionally relates to a motor vehicle and to a back end in which a method according to the invention or a device according to the invention is used. In a first step, data detected along a route traversed by the motor vehicle is received (10). The detected data is then divided (11) into segments of the traversed route, each of the segments being separated by a gap. Additionally, a spatial obfuscation is applied (12) to the data of the segments of the traversed route. The obfuscated data is finally forwarded (13) for further processing. The segmentation and the spatial obfuscation can be carried out within the motor vehicle or in a back end connected to the motor vehicle.