Vehicle Sensor PII Removal via Jurisdiction-Specific Semantic Labeling
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Solution Overview
Problem
Vehicle sensors capture sensitive personally identifiable information (PII) during connected features like sentry mode, which can violate data privacy laws depending on location, creating liability and customer annoyance, as existing solutions fail to intelligently separate allowable and prohibited data collection.
Innovation Solution
A system that uses sensors to capture data, performs object detection and semantic labeling, applies jurisdiction-specific PII filters to classify regions as public or private, and obfuscates or anonymizes PII in accordance with local laws, ensuring compliance with data privacy regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicle sensors capture data during connected features like sentry mode, then security monitoring capability is improved, but PII collection violates data privacy laws in certain jurisdictions
Solution Approach 1:
The sensor data is segmented into multiple regions based on semantic labeling (public vs. private areas). The system divides the captured data into distinguishable segments and applies different processing rules to each segment, allowing security monitoring in public areas while excluding private areas from PII collection.
Solution Approach 2:
Different quality standards are applied to different regions of the sensor data. Public areas are processed with full monitoring capability, while private areas are filtered to remove PII. This local differentiation allows the system to maintain security monitoring where appropriate while complying with privacy laws in private areas.
2Ease of operation
If existing solutions capture sensor data without intelligent separation, then data collection simplicity is maintained, but liability and customer annoyance increase due to privacy violations
Solution Approach 1:
The system performs preliminary processing by classifying regions as public or private before final data collection. This preliminary action identifies potential PII-containing regions in advance, allowing the system to prepare appropriate filtering actions beforehand and avoid liability issues without significantly complicating the overall data collection process.
Solution Approach 2:
An intermediary processing layer is introduced between sensor data capture and final data utilization. This intermediary layer performs semantic labeling and PII filtering, acting as a mediator that protects against privacy violations while maintaining the simplicity of the overall system architecture.
3Measurement precision
If the system processes sensor data through multiple classification stages, then PII removal accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The data processing is segmented into distinct stages: semantic labeling, public/private classification, and PII filtering. Each stage focuses on a specific aspect of the data, allowing for optimized processing at each level and improving overall accuracy while managing computational requirements through staged processing rather than monolithic analysis.
Data Source
AI summary
Removal of PII is provided. Sensor data is captured using sensors of a vehicle. Object detection is performed on the sensor data to create a sematic labeling of objects in the sensor data. A model is utilized to classify regions of the sensor data with a public or private labeling according to the sematic labeling and a PII filter corresponding to a jurisdiction of a current location of the vehicle. The sensor data is utilized in accordance with the public or private labeling.