Secure Attribute Selection With Group-Based Random Sampling
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Solution Overview
Problem
Existing methods for secret calculation to select an attribute with the best evaluation value are inefficient due to the need to calculate evaluation values for all attributes, resulting in a large calculation amount.
Innovation Solution
A secret attribute selection system that includes three or more secret attribute selection devices, which calculate a share of a column vector representing the attribute number of the best evaluation value among K attributes selected at random for each group, using matrix calculations and vector operations to efficiently determine the best attribute.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If evaluation values are calculated for all M attributes for each group, then an attribute having a best evaluation value can be selected, but the calculation amount becomes large
Solution Approach 1:
The patent divides the M attributes into multiple groups and randomly selects K attributes from each group for evaluation. This segmentation approach reduces the total number of attributes that need to be evaluated from M to K (where K < M), thereby decreasing the calculation amount while still maintaining the ability to select attributes with the best evaluation values through structured group-based sampling.
Solution Approach 2:
Instead of evaluating all M attributes, the patent performs partial evaluation by randomly selecting and evaluating only K attributes from each group. This partial action approach achieves acceptable attribute selection results with reduced computational effort, trading off complete evaluation for improved calculation efficiency.
2Productivity
If K attributes are selected at random from M attributes for each group, then the number of evaluation calculations is reduced, but the selection process becomes more complex
Solution Approach 1:
The patent performs preliminary random selection of K attributes from each group before the evaluation phase. This preliminary action organizes the attribute selection process in advance, creating a structured framework that simplifies the subsequent evaluation step while maintaining the benefits of reduced calculation complexity compared to evaluating all M attributes.
Data Source
AI summary
A first matrix calculation means that calculates a share of a matrix E in which each row satisfies a predetermined condition using a share of a matrix X representing N pieces of data and a share of a column vector →g representing groups obtained by grouping the N pieces of data, second matrix calculation means that calculates a share of a matrix Y and a share of a matrix U in which each row satisfies the predetermined condition, a third matrix calculation means that calculates a share of a matrix S in which each row satisfies the predetermined condition, and a first vector calculation means that calculates a share of a column vector →z with an attribute number of an attribute having a best evaluation value among K attributes selected at random in a group to which an i-th data belongs as an i-th element are included.