Data dimension compression method, data dimension compression system, secure computation device, and user terminal

The system efficiently performs dimensionality reduction using secure computation AI by computing encrypted data on plaintext, addressing inefficiencies in existing secure computation AI systems and enhancing their applicability and ease of integration.

WO2026126323A1 Publication Date: 2026-06-18NT T INC
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Patent Information

Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
NT T INC
Filing Date
2024-12-10
Publication Date
2026-06-18

AI Technical Summary

⚠Technical Problem

Existing secure computation AI systems for data analysis are inefficient due to computationally expensive processes like inverse matrix and eigenvalue/eigenvector calculations, preventing them from operating within acceptable computation times, especially when implementing linear discriminant analysis.

⚗Method used

A system comprising a secure computing device and a user terminal that performs dimensionality reduction using linear discriminant analysis by securely computing encrypted data, reducing the computational complexity through calculations on plaintext data, including secret sharing and secure aggregation functions to obtain transformation matrices.

🎯Benefits of technology

Enables efficient dimensionality reduction within acceptable computation times, expanding the applicability and ease of implementation of secure computation AI systems, while maintaining data confidentiality.

✦ Generated by Eureka AI based on patent content.

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Abstract

There is a desire for dimension compression of training data by linear discriminant analysis to be achieved by secure computation. An AI analysis model generation system according to the disclosed technology comprises a secure computation device and a user terminal. The secure computation device acquires data with n rows and m columns encrypted so as to be able to be securely computed, the data comprising n m-dimensional vectors to which class classifications are assigned. For each class, encrypted vectors belonging to the class are securely computed to obtain an encrypted sum vector and an encrypted sum-of-products matrix. The user terminal decrypts the encrypted sum vector and the encrypted sum-of-products matrix to obtain a plaintext sum vector and a plaintext sum-of-products matrix, uses the plaintext sum vector and the plaintext sum-of-products matrix to obtain a within-class scatter matrix and a between-class scatter matrix, and uses the within-class scatter matrix and the between-class scatter matrix to obtain a transformation matrix with m rows and d columns.
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