Autonomous Vehicle Data Classification for Passenger Privacy Control
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Solution Overview
Problem
Autonomous vehicles lack effective methods to protect passenger data privacy, as they collect sensitive information such as biometric data, navigation details, and external surroundings, without providing users with control over how this data is used or secured.
Innovation Solution
A system that classifies data inputs from vehicle sensors based on user-defined preferences, applying encryption and obfuscation to ensure privacy, allowing users to select data security actions such as deletion or temporary storage, and controlling data disposition at the end of a trip.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles collect comprehensive sensor data for navigation and operation, then vehicle operational reliability is improved, but user privacy security deteriorates
Solution Approach 1:
The patent segments sensor data into multiple classification categories (e.g., operational data, personal data, sensitive data) and applies different security measures to each category. This allows the vehicle to maintain comprehensive data collection for operational reliability while protecting user privacy through targeted security actions on specific data types.
Solution Approach 2:
The patent implements local quality by applying different security preferences and protection levels to different portions of collected data based on their classification. Critical operational data receives minimal processing to maintain vehicle functionality, while personally identifiable information receives enhanced security measures such as encryption or deletion, thus resolving the contradiction between comprehensive data collection and privacy protection.
2Adaptability or versatility
If user-defined data security preferences are implemented, then user privacy control is improved, but data processing complexity increases
Solution Approach 1:
The patent applies preliminary action by establishing user data security preferences before data collection begins. Users define their privacy requirements in advance, and the system pre-configures security rules and classification criteria. During operation, the vehicle automatically applies these pre-set preferences to incoming sensor data, reducing real-time processing complexity while maintaining high user control and adaptability.
3Object-affected harmful factors
If data is encrypted and obfuscated for security, then user privacy protection is improved, but data utility for analysis deteriorates
Solution Approach 1:
The patent applies local quality by selectively encrypting or obfuscating only the portions of data that contain personally identifiable information or sensitive user data, while leaving operational and non-sensitive data in its original form. This ensures that data utility for vehicle operation and analysis is maintained, while user privacy protection is applied precisely where needed.
Solution Approach 2:
The patent segments data into categories based on sensitivity levels and applies different processing treatments to each segment. Operational data required for vehicle function remains unencrypted and fully utilizable, while personal information segments receive encryption or obfuscation. This segmentation approach preserves data utility for essential operations while providing targeted privacy protection.
Data Source
AI summary
Data privacy for data associated with traveling in a vehicle that has access to passenger data, vehicle location data, vehicle navigation data, peripheral data; and/or itinerary data. Privacy is achieved by data classification and corresponding data security preferences and/or user selection including end of trip data disposition actions.