Secure Grouping Apparatus for Decision Tree Learning
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Solution Overview
Problem
The conventional method for learning a decision tree through secret calculation requires sorting elements of the vector xj', increasing calculation costs when further grouping data.
Innovation Solution
A secure grouping apparatus that inputs a hidden value of an objective variable vector y, a permutation σj for stably sorting the j-th explanatory variable vector, and a grouping result vector b, and calculates a new grouping result vector y', permutation σj', and explanatory variable vector vj' without sorting xj', thereby reducing calculation costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If sorting is performed to calculate the permutation σj' in secret calculation, then the grouping accuracy is improved, but the calculation cost increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing the permutation σj that sorts the j-th explanatory variable vector xj before the actual grouping operation. This pre-computed permutation is then reused during the grouping process, eliminating the need to perform sorting again when calculating σj'. The pre-computed σj serves as a foundation that accelerates subsequent operations while maintaining grouping accuracy.
Solution Approach 2:
The patent uses copying by creating and utilizing a copy of the permutation σj for calculating σj'. Instead of重新 performing the sorting operation, the existing permutation σj is copied and applied to the grouped data structure. This copying approach preserves the sorting results without incurring additional sorting costs, thereby reducing calculation complexity while maintaining the integrity of the grouping operation.
2Stability of the object's composition
If the elements of vector xj' are sorted to obtain permutation σj' in secret calculation, then the stability of grouping is improved, but the calculation time increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating the permutation σj that sorts the j-th explanatory variable vector xj before the actual grouping operation. This pre-computed permutation is then reused during the grouping process, eliminating the need to perform sorting again when calculating σj'. The pre-computed σj serves as a foundation that accelerates subsequent operations while maintaining grouping accuracy.
Solution Approach 2:
The patent inverts the conventional approach by not directly sorting xj' to obtain σj'. Instead, it uses the inverse logic: the permutation σj that already exists from previous sorting is applied to the grouped indices to derive σj'. This inversion transforms a potentially expensive sorting operation into a simpler permutation application, reducing calculation time while preserving grouping stability.
Data Source
AI summary
A secure grouping apparatus according to an embodiment is a secure grouping apparatus that is given training data including a plurality of samples having m explanatory variables as a hidden value of an objective variable vector z having a value of an objective variable of each sample as an element and an explanatory variable vector wj (j∈[1, m]) having a value of a j-th explanatory variable of each sample as an element, groups the samples included in the training data into groups, and executes grouping to arrange samples in the same group to be continuous in secure calculation, the secure grouping apparatus including an input unit that inputs a hidden value of an objective variable vector y of the grouped training data, a hidden value of a permutation σj for stably sorting a j-th explanatory variable vector of the grouped training data in a group, a hidden value of an explanatory variable vector vj obtained by stably sorting the j-th explanatory variable vector of the grouped training data in the group, and a hidden value of a grouping result vector b representing a grouping result when the training data is grouped into groups under a predetermined condition; and a secure grouping unit that calculates, in secure calculation, a hidden value of an objective variable vector y′ of the training data newly grouped according to a grouping result represented by the grouping result vector, a hidden value of a permutation σj′ for stably sorting the j-th explanatory variable vector of the training data newly grouped according to the grouping result in a group, and a hidden value of an explanatory variable vector vj′ obtained by stably sorting the j-th explanatory variable vector of the training data newly grouped according to the grouping result in a group.