Hashing Structural Data via Key-Value Sorting
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Solution Overview
Problem
Existing hash functions fail to produce consistent hash values for structural data represented in different data formats, programming languages, and platforms, leading to inconsistencies in data integrity validation across various entities in industries like cloud computing and e-commerce.
Innovation Solution
A method for hashing structural data that involves parsing key-value pairs from the structural data and computing a hash value based on a predefined policy, which includes sorting the key-value pairs, ensuring consistent hash values across different data formats and platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing hash functions are used on structural data in different data formats, then data processing is simple, but hash values are inconsistent leading to data integrity validation failures
Solution Approach 1:
The patent applies preliminary action by sorting key-value pairs before hashing. This preprocessing step ensures that structurally equivalent data regardless of format produces consistent hash values. The sorting operation is performed in advance to eliminate format-related inconsistencies, allowing the hash function to operate on normalized data.
Solution Approach 2:
The patent changes the parameter ordering of key-value pairs through sorting before hashing. By transforming the input data structure into a standardized sorted sequence, the hash function receives consistent input regardless of the original data format, thereby producing consistent hash values for semantically equivalent data.
2Reliability
If hash values are computed without sorting key-value pairs, then processing is faster, but hash values differ for equivalent data in different formats
Solution Approach 1:
The sorting operation is performed as a preliminary step before hash computation. This preliminary action organizes the data in a standardized manner, ensuring that subsequent hash computations on equivalent data structures yield consistent results, thereby enabling reliable data integrity validation.
Solution Approach 2:
The patent transforms the input parameters by sorting key-value pairs before hashing. This parameter transformation ensures that the hash function operates on consistently ordered data, making hash values comparable across different data formats while maintaining processing efficiency.
3Adaptability or versatility
If a predefined policy including sorting is applied, then hash values are consistent across platforms, but the hashing process becomes more complex
Solution Approach 1:
The patent implements a universal hashing approach by sorting key-value pairs before hashing. This standardized preprocessing step makes the hash function adaptable to data from different platforms and formats, ensuring consistent hash values across diverse environments while maintaining a relatively simple overall process.
Solution Approach 2:
The patent applies parameter changes through sorting to achieve cross-platform compatibility. By transforming the input data into a standardized sorted sequence, the hash function becomes versatile and platform-independent, producing consistent results regardless of the source platform or data format.
Data Source
AI summary
Provides a method for hashing structural data. According to this method, structural data that includes a plurality of keys and a plurality of values corresponding to respective keys is received. A plurality of key-value pairs can be parsed from the structural data, with each of the plurality of key-value pairs including a key and a value respectively corresponding to the key. A hash value of the plurality of key-value pairs is computed according to a predefined policy as a hash value of the structural data, wherein the predefined policy includes sorting the plurality of key-value pairs or processed key-value pairs.