Mixed-Data Hash Verification Against Quantum Substitution Attacks
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Solution Overview
Problem
Existing hashing techniques are vulnerable to attacks, especially by quantum computers, and do not adequately ensure the integrity and authenticity of data transmission, allowing for potential substitution of data without detection.
Innovation Solution
A method involving mixing a dataset with a mixer number using a mixing function and then hashing the mixed data, followed by comparing the resulting hash with a similarly processed reference dataset, enhances security by making it improbable for similar data to have the same hash, thus ensuring data integrity and authenticity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional hash functions (MD5, SHA1, SHA256) are used to verify data integrity, then the verification process is simple and efficient, but the security is vulnerable to attacks especially by quantum computers that can bypass these hash functions
Solution Approach 1:
The patent segments the hashing process into two distinct stages: first applying a mixing function to the input data to produce mixed data, then applying a hash function to the mixed data. This segmentation creates an additional security layer that resists quantum computing attacks while maintaining verification reliability.
Solution Approach 2:
The patent applies the mixing function as a preliminary action before the hash function. This preliminary transformation of data into mixed data creates a security barrier that prevents direct attacks on the hash function, including quantum computing attacks, while preserving the integrity verification capability.
2Reliability
If data is encrypted entirely with a key (one-time pad method), then security is maximized, but the key management becomes complex and requires keys as long as the data itself
Solution Approach 1:
The patent extracts only the essential security verification element (hash of mixed data) from the complete data set. This allows verification of data integrity without requiring encryption of the entire data set, thereby reducing key management complexity while maintaining security for the verification purpose.
Solution Approach 2:
The patent creates a cryptographic copy (hash) of the transformed data instead of working with the complete original data. This copy serves as a verification token that is much smaller and easier to manage than the original data, reducing key management complexity while preserving security verification capability.
3Device complexity
If hash functions are used to create compact verification data, then key management is simplified, but it becomes possible to create similar data with the same hash, allowing undetected substitution
Solution Approach 1:
The patent applies the mixing function as a preliminary transformation before hashing. This preliminary action increases the complexity of the input to the hash function, making it computationally infeasible to find different data that produces the same hash value, thereby preventing undetected substitution attacks.
Solution Approach 2:
The patent changes the parameter space by transforming the original data through a mixing function before hashing. This parameter transformation expands the complexity of the input, making collision attacks (finding different data with the same hash) practically impossible while maintaining the compact verification benefit.
Data Source
AI summary
A method, performed in an environment where data transmission is vulnerable to quantum computers, for comparing a first dataset and a second dataset, in particular with a view for determining whether these two datasets are identical. The method not requiring the presence of these two datasets in the apparatus, and including the following steps of: a) mixing a number, called the mixer number, with the first dataset, using a mixing function, in order to obtain mixed data, b) hashing the mixed data using a hash function, wherein the a length of the mixer number is longer than a length of the hash and c) comparing the hash thus obtained in step b) with a third dataset assumed to be the hash of the second dataset mixed with the same mixer number as that used in step a) and with the same mixing function.