Vehicle-to-Cloud Operation Sequencing for PII Leakage Reduction
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Solution Overview
Problem
As vehicle-mounted sensors generate high-quality data that can identify individuals, there is a risk of personally identifiable information (PII) leakage, and existing systems lack effective methods to balance data filtering and compression across vehicle-to-cloud networks to minimize this risk.
Innovation Solution
A system that determines program operation sequences for filtering and compression at different stages of the vehicle-to-cloud network, allocating resources to minimize PII leakage by assessing computational and transmission costs, and using on-demand data retrieval (ODDR) to manage data requests from users, ensuring data requests are agnostic to their origin and sensor types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-quality sensor data is stored and transmitted to maintain data quality for ADAS training and simulation, then the quality of information increases, but the risk of PII leakage increases
Solution Approach 1:
The data processing pipeline is segmented into multiple stages (vehicle-side filtering, compression, cloud-side processing) where different operations are applied at different stages. This allows the system to maintain data quality for ADAS training while progressively reducing PII content through targeted filtering and compression operations at appropriate stages.
Solution Approach 2:
Different quality levels and processing intensities are applied to different parts of the data pipeline. Critical data for ADAS training maintains high quality, while data segments with higher PII risk undergo more aggressive filtering and compression. This local differentiation allows the system to optimize the trade-off between data quality and PII reduction.
2Object-affected harmful factors
If data filtering and compression operations are applied to reduce PII content, then the likelihood of PII leakage decreases, but the computational and transmission costs increase
Solution Approach 1:
Data filtering and compression operations are performed preliminarily at the vehicle stage before transmission to the cloud. This preliminary action reduces the volume of data requiring cloud processing, thereby reducing overall computational and transmission costs while still effectively reducing PII content.
Solution Approach 2:
The system dynamically adjusts the intensity and type of filtering and compression operations based on the specific data characteristics, sensor type, and processing stage. This dynamic adaptation allows the system to apply minimal necessary processing to reduce PII while avoiding excessive computational and transmission costs.
3Adaptability or versatility
If comprehensive data collection is performed to meet diverse user requests, then the versatility of data retrieval increases, but the complexity of managing data requests and priorities increases
Solution Approach 1:
The data processing system is designed with universal operations that can handle diverse user requests across different sensor types and data modalities. The same filtering and compression framework serves multiple purposes (ADAS training, simulation, user data requests), reducing the need for separate specialized processing pipelines.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor data request patterns, user priorities, and processing effectiveness. This feedback allows the system to automatically adjust data collection and processing strategies, reducing manual management complexity while maintaining high versatility in meeting diverse user needs.
Data Source
AI summary
Program operation sequence determination for reduced potential leakage of personally identifiable information is performed by identifying a plurality of candidate program operations for capturing a data sample including first class information and second class information and reducing the second class information of the data sample, assigning a leakage cost representing potential leakage of personally identifiable information associated with each valid combination of a candidate program operation among the plurality of candidate program operations and a computational resource among a plurality of computational resources, and applying an objective function to the valid combinations and assigned leakage costs to determine a sequence of program operations, wherein each program operation of the sequence is performed by one or more selected computational resources such that the sum leakage cost is below a threshold leakage cost, and the amount of first class information is above a data threshold value.