Vehicle Data Anonymization for Privacy Compliance
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Solution Overview
Problem
Current data acquisition methods in vehicles face challenges in preventing indirect access to protected data, particularly personal data, which is a concern due to legal regulations like GDPR, as vehicle data can inadvertently reveal information about drivers, passengers, and other road users, making it difficult to ensure data security during testing and development of autonomous driving systems.
Innovation Solution
A preprocessing operation is implemented in a processing unit within the vehicle to modify vehicle data records, ensuring a predetermined degree of anonymity is met, thereby preventing indirect conclusions about protected data by applying methods such as normalization, anonymization, and resolution reduction, and storing the modified data as secured vehicle data records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If vehicle data is collected and stored for autonomous driving development and analysis, then the quality and quantity of data for system improvement is improved, but the risk of indirect access to protected personal data increases
Solution Approach 1:
The patent applies preliminary action by performing anonymization processing on vehicle data before storage and analysis. The data is transformed in advance to remove or generalize personal identifiers, so that when the data is later used for autonomous driving development, it cannot be traced back to specific individuals. This preventive measure ensures data protection is built into the data lifecycle from the beginning.
Solution Approach 2:
The patent introduces an intermediary processing layer between data collection and data storage/analysis. This intermediary component performs anonymization transformations, acting as a mediator that separates the raw personal data from the analytical processes. The anonymized data serves as an intermediate representation that maintains analytical value while eliminating direct personal identification capabilities.
2Measurement precision
If detailed vehicle data including position and behavior patterns is recorded, then the accuracy of autonomous driving system testing is improved, but the ability to identify individual drivers and passengers increases
Solution Approach 1:
The patent applies local quality by selectively anonymizing specific data fields while preserving others. Position data, timing information, and driving behavior patterns are retained at full resolution for accurate system testing, while only the personally identifiable elements are generalized or removed. This selective approach maintains measurement precision where needed while protecting privacy in critical areas.
Solution Approach 2:
The patent transforms data parameters through anonymization operations such as generalizing position coordinates to broader geographic zones, aggregating timing data to less precise time intervals, or removing unique behavioral signatures. These parameter changes reduce the identifiability of individual drivers while preserving the statistical and pattern information necessary for autonomous driving system evaluation.
3Productivity
If vehicle data is transmitted to external test centers for evaluation, then the capability for comprehensive data analysis is improved, but the security risk and compliance burden increase
Solution Approach 1:
The patent performs anonymization processing before data transmission to external test centers. By pre-processing the data to remove personal identifiers, the system reduces the security and compliance burden associated with transmitting sensitive personal information externally, while still enabling comprehensive analytical evaluation at the test center.
Solution Approach 2:
The patent extracts and removes personally identifiable information from the vehicle data before external transmission. This extraction process separates the sensitive personal data elements from the analytical data, allowing the anonymized dataset to be transmitted for comprehensive analysis without carrying the same security risks and compliance requirements as raw personal data.
Data Source
AI summary
Disclosed is a method for data acquisition in a vehicle, comprising at least one data acquisition unit and at least one processing unit, wherein the at least one data acquisition unit records at least one vehicle data record that is marked by at least one protected vehicle data record. The vehicle data is modified on the basis of a degree of anonymity by a preprocessing operation in the processing unit and is stored as secure vehicle data such that it is impossible to draw conclusions about the protected vehicle data.