Vehicle Data Validation Using Random Assembly Rules
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Solution Overview
Problem
Existing methods struggle to reliably validate the integrity of vehicle data, as manipulated data can easily be masqueraded as real data due to predictable data collection patterns, posing security and authenticity risks in applications requiring accurate vehicle information.
Innovation Solution
A method involving a configuration file transmitted to a data collecting device that requests specific vehicle data, assembles them in a random and unpredictable order using a prescribed data assembling rule, and verifies the assembly against a known rule to ensure data integrity, using encryption and dynamic rule changes for enhanced security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicle data is collected using standard predictable patterns, then data collection is simple and systematic, but data integrity cannot be guaranteed as manipulated data can easily be masqueraded as real data
Solution Approach 1:
The patent applies dynamics by making the data collection pattern dynamic and unpredictable. Instead of fixed periodic sampling, the system randomly selects data packages to collect at random time intervals. This dynamic approach prevents attackers from predicting when and what data will be collected, thereby ensuring data integrity while maintaining a relatively simple collection mechanism.
Solution Approach 2:
The patent implements preliminary action by pre-defining multiple data package types (A, B, C, etc.) with specific properties before data collection begins. The evaluation unit also pre-knows the expected random selection pattern. This preliminary setup allows the system to validate data authenticity without adding complex real-time processing, resolving the contradiction between reliability and complexity.
2Reliability
If data packages are collected in a fixed standard pattern, then data processing is efficient, but security and authenticity risks increase due to predictability
Solution Approach 1:
The system dynamically randomizes the selection of data packages and their collection timing, making the data collection pattern unpredictable to attackers. The evaluation unit efficiently processes this random pattern by comparing it against pre-defined expectations, thus maintaining processing efficiency while significantly improving data authenticity through unpredictability.
3Reliability
If manipulated vehicle data is generated on external servers, then simulation data can be created, but it becomes indistinguishable from real vehicle data without validation
Solution Approach 1:
The patent uses preliminary action by pre-defining the expected random data collection pattern at the evaluation unit before data arrives. When data packages are received, the system checks if they match the pre-defined random pattern. Manipulated data generated on external servers cannot replicate this specific random pattern, making detection straightforward despite the difficulty of distinguishing manipulated from real data in traditional systems.
Solution Approach 2:
The system implements feedback by continuously monitoring whether received data packages conform to the expected random selection pattern. The evaluation unit compares actual data collection patterns against the pre-defined random pattern and provides feedback on validation results. This feedback mechanism enables reliable detection of manipulated data while maintaining ease of implementation.
Data Source
AI summary
The invention comprises a method for validating vehicle data (22) of a designated vehicle and a vehicle that is designed to conduct corresponding steps of this method. The method comprises transmitting a configuration file (16) to a data collecting device (18) of a presumed vehicle (20), wherein the configuration file (16) comprises details on the wanted specific vehicle data (22) (S1). The data collecting device (18) then retrieves the requested specific vehicle data (22) accordingly and assembles it as a specific data item (24) according to a prescribed data assembling rule (S2, S3). Afterwards, the specific data item (24) is transmitted to an evaluation unit (14) to be disassembled (S4). Only if the transmitted specific data item (24) features the prescribed data assembling rule, the disassembled specific vehicle data (22) is validated and provided as vehicle data (22) of the designated vehicle (S5, S6).