Matrix Transposition Masking Against Quantum and Side-Channel Attacks
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Solution Overview
Problem
Existing data processing and encryption methods are vulnerable to attacks such as brute force and side channel attacks, particularly when using quantum computing techniques, during matrix transposition operations.
Innovation Solution
Implement a matrix transposition method that includes generating shifted vectors, logically combining them, and applying masking operations to protect the data, such as using EXCLUSIVE OR functions and shifting techniques to obscure the original data during transposition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If matrix transposition is performed using conventional methods, then data processing efficiency is maintained, but data security is compromised due to vulnerability to brute force and side channel attacks
Solution Approach 1:
The patent applies preliminary masking to the matrix data before transposition operations are performed. Random masks are generated and applied to the input matrix, ensuring that even if an attacker gains access to intermediate values during processing, the original data remains protected. This preliminary protective action resolves the contradiction by establishing security before the vulnerability to attacks occurs.
Solution Approach 2:
The patent introduces random masks as intermediary elements between the original data and the transposition process. These masks act as mediators that obscure the relationship between input and output data, preventing direct inference attacks. The masks are systematically applied and removed, allowing correct transposition while maintaining security, thus resolving the contradiction between security and processing complexity.
2Reliability
If masking operations are applied to protect data during matrix transposition, then data security is improved, but processing time and computational overhead increase
Solution Approach 1:
The patent changes the parameter of data representation by applying masking transformations. The masking operation modifies the data parameters (adding random values) in a way that preserves the mathematical structure needed for transposition while hiding the original values. This parameter change enables security without requiring fundamentally different processing algorithms, thus minimizing time overhead.
Solution Approach 2:
The patent employs periodic masking and unmasking operations that are systematically applied throughout the transposition process. Rather than continuous complex protection, the masking is applied at specific intervals and stages, allowing efficient processing between masking operations while maintaining security during vulnerable phases, thus resolving the time overhead issue.
3Object-affected harmful factors
If random masks are generated and applied to matrix vectors, then protection against quantum computing attacks is enhanced, but the complexity of the processing operation increases
Solution Approach 1:
The patent segments the matrix data into vectors and applies masking operations to individual vectors or groups of vectors. This segmentation allows the complex security operation to be broken down into manageable steps that can be processed efficiently. Each vector is masked independently, reducing the overall computational complexity compared to masking the entire matrix at once, thus resolving the contradiction between security and complexity.
Data Source
AI summary
A cryptographic operation is protected. The protecting includes performing a matrix transformation operation on a matrix having n rows and n columns, each row forming a respective vector of a first set of ordered vectors. A second set of ordered vectors is generated by shifting values of vectors of the first set of ordered vectors in a first direction, wherein a pitch of a shift applied to a vector of the first set of ordered vectors is based on an order number of the vector of the first set of ordered vectors. A working vector is generated by logically combining vectors of the second set of ordered vectors. A third set of ordered vectors is generated based on the second set of ordered vectors. A fourth set of ordered vectors is generated based on the third set of ordered vectors and the working vector.