Adversarial Validation for Safety-Critical Blockchain
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Solution Overview
Problem
Current blockchain validation processes are not robust enough to meet the needs of safety-critical systems, as they rely on a single validation technique across all validators, which is inadequate for ensuring transaction security and accuracy, especially under unfavorable conditions.
Innovation Solution
Implementing an adversarial validation strategy that uses multiple algorithms with different criteria and weightings for facial and fingerprint recognition, allowing validators to perform independent calculations and achieve consensus through a multi-channel voting process, with algorithms such as facial recognition using eye color, skin tone, and retinal features, and fingerprint recognition using various criteria, to enhance transaction validation robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single validation technique is used across all validators, then the validation process is simple and consistent, but the robustness and accuracy of transaction validation is insufficient for safety-critical systems
Solution Approach 1:
The validation process is segmented into multiple independent validation techniques (e.g., facial recognition, fingerprint recognition, document verification). Each validator can select and execute one or more of these segmented validation methods independently, allowing the system to achieve higher reliability through diverse validation approaches while maintaining manageable complexity through modular design.
Solution Approach 2:
The system uses a composite validation approach where multiple different validation techniques are combined into a unified validation framework. Similar to how composite materials combine different materials to achieve superior properties, this composite validation strategy combines multiple validation techniques to achieve robustness and accuracy that exceeds any single technique alone.
2Measurement precision
If multiple validation algorithms with different criteria are used, then the accuracy and robustness of validation improves, but the computational complexity and processing time increases
Solution Approach 1:
The system dynamically selects and adjusts validation algorithms based on transaction characteristics, risk levels, and contextual factors. Rather than always applying all possible validation algorithms, the system adapts the validation process in real-time, applying only the necessary algorithms for each specific transaction, thereby maintaining high accuracy while reducing unnecessary processing time.
Solution Approach 2:
The system changes validation parameters such as the number and type of algorithms applied, threshold values, and verification depth based on transaction risk assessment. For low-risk transactions, fewer algorithms with lower thresholds are used, while high-risk transactions trigger more comprehensive validation with multiple algorithms and stricter criteria, optimizing the balance between accuracy and processing time.
3Reliability
If adversarial validation strategies are implemented, then false positives and negatives are reduced, but the system complexity and implementation difficulty increases
Solution Approach 1:
The system introduces intermediary components such as validation orchestrators, algorithm registries, and result aggregation services that manage the complex interactions between multiple validation algorithms. These intermediaries simplify the implementation by providing standardized interfaces and coordination mechanisms, making the adversarial validation system easier to deploy and maintain while preserving its high accuracy benefits.
Data Source
AI summary
A computing system architecture includes a service server and a first validator. The first validator is communicable to the service server. A validation request is sent from the service server to the first validator for validating a transaction. The validation request including a first facial image of a party of the transaction. The validator performs a facial recognition using the first facial image of the party for validating the transaction.