Combining Hashes of Data Blocks for Integrity Verification
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Solution Overview
Problem
Existing secure hashing techniques require all data blocks to be accessible in the correct order for computing the overall secure hash, leading to inefficiencies and rigidity in combining hash values, particularly in scenarios where data integrity and resistance against illicit modification are critical.
Innovation Solution
The proposed solution involves calculating a hash value for each data block, combining these values in a flexible manner using associative and non-commutative operations, such as matrix multiplication or string concatenation, to produce a master hash value that retains the sequence of data blocks, while employing cryptographic hash functions for pre-image resistance, second pre-image resistance, and collision resistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all data blocks are made accessible in the correct order for computing the overall secure hash, then data integrity verification is ensured, but processing efficiency and flexibility deteriorate
Solution Approach 1:
The patent divides the data into multiple blocks and computes individual hash values for each block separately. These hash values are then combined using associative operations to produce the overall hash, eliminating the need to process all data blocks sequentially while maintaining integrity verification.
Solution Approach 2:
The patent pre-computes hash values for individual data blocks and stores them separately. When verification is needed, these pre-computed hash values can be combined directly without re-processing the original data blocks, significantly improving verification efficiency.
2Ease of manufacture
If rigid combination methods are used for hash values, then computational simplicity is maintained, but adaptability and flexibility deteriorate
Solution Approach 1:
The patent employs associative combination operations that can work with different data structures and formats. The same combination mechanism can handle various scenarios including sequential data, distributed data, and parallel processing results, providing both simplicity and adaptability.
Solution Approach 2:
The patent allows the combination operation to be applied in different orders and contexts while maintaining the same result due to the associative property. This dynamic flexibility enables the system to adapt to different processing architectures without changing the fundamental combination logic.
Data Source
AI summary
Example embodiments relate to combining hashes of data blocks. The examples disclosed herein calculate a hash value for each data block in a sequence of data blocks. The hash values are combined into a combined value, where the combined value has the same sequence as the sequence of data blocks. A master hash value is then calculated for the combined value.