Application Certification Using Semantic Check Code Subsets
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Solution Overview
Problem
Current data validation methods are inefficient in distinguishing non-significant modifications from significant changes in data, leading to unnecessary certification processes and increased costs, particularly in interactive television applications where minor content changes require full re-certification.
Innovation Solution
A method that computes check codes for data subsets determined by semantic analysis, using a mask to select relevant data elements and ignoring non-significant modifications, ensuring the integrity of the remaining content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a full data validation algorithm is applied to verify data integrity, then the reliability of detecting modifications is improved, but the computational cost and time consumption increase
Solution Approach 1:
The patent divides the data into multiple blocks and applies validation algorithms selectively to different blocks based on their significance. Critical blocks undergo full validation while non-critical blocks use simplified validation, reducing overall computation time while maintaining reliability for important data portions.
Solution Approach 2:
Different validation strategies are applied to different portions of the data based on their importance. The patent identifies and applies stricter validation to critical data blocks while using lighter validation for non-critical blocks, optimizing the balance between detection reliability and processing efficiency.
2Productivity
If a simple validation algorithm is used to reduce computational cost, then the processing speed is improved, but the ability to detect deliberate tampering deteriorates
Solution Approach 1:
The patent segments data into critical and non-critical blocks, applying simple validation to non-critical blocks for speed while applying robust validation to critical blocks for tamper detection, achieving both processing efficiency and security.
Solution Approach 2:
The patent applies different validation qualities to different data portions - simple checksums for non-critical data and cryptographic validation for critical data - optimizing the balance between processing speed and tamper detection capability.
3Reliability
If all modifications are treated as significant requiring full re-certification, then the data integrity is ensured, but the deployment efficiency of applications with minor changes deteriorates
Solution Approach 1:
The patent divides applications into critical and non-critical components, allowing selective re-certification of only the modified critical portions rather than requiring full re-certification of the entire application, thereby improving deployment efficiency while maintaining integrity.
Solution Approach 2:
The patent applies partial validation - only the minimum necessary validation required for the level of modification - rather than excessive full validation. Minor changes in non-critical blocks require only localized validation, avoiding unnecessary re-certification overhead.
4Reliability
If a comprehensive validation of all data blocks is performed, then the security against deliberate modification is improved, but the computational resources required increase
Solution Approach 1:
The patent segments data into blocks with different security requirements, applying energy-intensive cryptographic validation only to critical blocks while using energy-efficient checksums for non-critical blocks, reducing overall computational energy consumption while maintaining security.
Solution Approach 2:
The patent applies different validation qualities to different data portions based on their security requirements. Critical blocks receive robust cryptographic validation while non-critical blocks use simpler validation, optimizing the balance between security and computational energy usage.
Data Source
AI summary
A first check code is computed by applying an algorithm to a proper subset of a first body of data. A second check code is computed by applying the algorithm to an equivalent proper subset of a second equivalent body of data. The two check codes are compared. The extent of the proper subset of the first body of data is determined by a semantic analysis of the first body of data. Multiple versions of an application, when the semantic changes between the applications are inconsequential, may then be certified by ignoring the non-significant modifications and ensuring the integrity of the remainder of the content.