Masked Zero Comparison Using Partitioned Intermediate Data
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Solution Overview
Problem
Existing cryptographic algorithms are insecure against quantum computers and require heavy computational load for zero comparisons, especially when dealing with secret data that must remain secure from side-channel attacks.
Innovation Solution
A method for zero-safe comparison of masked input data using a modular additive masking with a partitioned set of intermediate data, allowing secure zero comparisons without converting to Boolean masking, suitable for electronic devices with limited resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If modular additive masking is used for zero comparison of secret data, then security against side-channel attacks is improved, but computational load increases
Solution Approach 1:
The patent segments the modular additive masking into multiple components (first set of first parts, second set of second parts, third set of third parts) and processes them separately through distinct computational paths. This segmentation allows the system to maintain security properties while reducing the overall computational burden by avoiding full conversion to Boolean masking for all components.
Solution Approach 2:
The patent introduces an intermediary representation (the second set of second parts in modular additive masking) that bridges between the input masked data and the final Boolean comparison result. This intermediary allows computations to be performed in a more efficient modular arithmetic domain before final conversion, reducing computational load while maintaining security.
2Productivity
If conversion to Boolean masking is performed for zero comparison, then computational load is reduced, but security against side-channel attacks deteriorates
Solution Approach 1:
The patent applies segmentation by dividing the masking representation into multiple sets (first, second, and third sets) with different properties. The second set maintains modular additive masking for security, while the third set uses Boolean masking for efficient comparison. This selective segmentation allows the system to balance security and computational efficiency.
Solution Approach 2:
Different parts of the data are treated with different masking qualities. The first parts are processed with modular additive masking for security-critical operations, while the third parts use Boolean masking for computationally intensive comparison operations. This local differentiation of masking quality optimizes both security and performance.
3Reliability
If high-order polynomial comparison is used, then security is improved, but device complexity and computational load increase
Solution Approach 1:
The patent segments the polynomial comparison process into distinct stages with different masking representations. By dividing the computation into multiple steps with intermediate representations, the system maintains the security benefits of high-order polynomial comparison while reducing overall process complexity through modular organization.
Data Source
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AI summary
A zero-safe comparison method of a masked input data in the form of a first set of n first parts of a modular additive masking, n being an integer strictly greater than 1, and the method comprising the following steps: - Determination (E2) of a second set of intermediate data from the first parts, - Determination (E4) of a third set of third parts of a Boolean masking of the result of the comparison from the intermediate data, the method being characterized in that the second set has a partition into a first subset and a second subset such that a first result obtained by combination with exclusive or zero and intermediate data from the first subset, is equal to a second result obtained by combination with exclusive or zero and intermediate data from the second subset, when and only when the input data is 0.