Method and device for jointly generating data tuples for secure computing

Abstract

The embodiments of this specification provide a method and an apparatus for jointly generating data tuples for secure computing. According to this method, the first party obtains a common vector formed by c common polynomials in the polynomial ring, and randomly generates c first polynomials to form a random vector. Then, the first party and the second party execute the function secret sharing protocol, and obtain the fragmentation vector formed by c2 first fragmentation polynomials, and the sum of the corresponding fragmentation obtained by the second party and the c2 polynomials of both parties corresponds to c2 product polynomials. Each polynomial in each vector is then converted to a point-valued representation of the N root points of the modulo polynomial F(X) in the polynomial ring. Thus, the point value sequence corresponding to the inner product result of the random vector and the common vector, and the point value sequence corresponding to the inner product result of the multiplication vector and the slice vector obtained by performing tensor multiplication on the common vector itself are determined. Combine the point values ​​of the two point value sequences in turn to obtain N data tuples.

Description

technical field [0001] One or more embodiments of this specification relate to the field of data privacy security, and in particular to a method and an apparatus for generating a data tuple used for secure computing. Background technique [0002] With the development of computer technology, machine learning has been applied to various technical fields to analyze and process various business data. The data needed for machine learning often involves multiple fields. For example, in the scenario of merchant classification analysis based on machine learning, the electronic payment platform has the transaction flow data of the merchants, the e-commerce platform stores the sales data of the merchants, and the banking institution owns the merchants. loan data. Data often exists in silos. Due to issues such as industry competition, data security, and user privacy, data integration faces great resistance, and it is difficult to integrate data scattered across various platforms for ...

AI Technical Summary

Problems solved by technology

Due to issues such as industry competition, data security, and user privacy, data integration faces great resistance. It is difficult to integrate data scattered across various platforms for machine learning model training.

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