Matrix QR decomposing method based on matrix privacy protection

Abstract

The invention discloses a matrix QR decomposing method based on matrix privacy protection. The method comprises the steps that an original matrix is preprocessed, the processed matrix is sent to a service provider, QR decomposing is conducted on the matrix by the service provider, the decomposed matrix is returned to a user, and after the user conducts certain operation, the matrix after decomposition of the original matrix is restored. Through above mode, according to the matrix QR decomposing method based on matrix privacy protection, due to the fact that the matrix is preprocessed, the service provider can not acquire any information of the original matrix, and the safety of the information and the transmission safety are improved.

Description

technical field [0001] The invention belongs to the field of privacy protection, and in particular relates to a matrix QR decomposition method based on matrix privacy protection. Background technique [0002] With the development of computer science, more and more problems that computers can handle are becoming more and more complex. Therefore, with the emergence of cloud computing, outsourcing and other methods, computing resources are also provided to users as a service, and users will correspondingly increase Many tasks are handed over to these service providers, so privacy has become a concern, because a lot of data is private, but the computing resources for processing these data are insufficient, so we have to use external computing resources, which is the so-called Therefore, before the data is handed over to the service provider for processing, the data should be preprocessed first, such as homogeneous encryption, and then handed over to the service provider after pr...

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Problems solved by technology

[0003] Decomposing a matrix is ​​a very common problem, and its application is also very wide. Whether it is in engineering or in theory, such as: pattern recognition, data mining, solving eigenvalues, solving linear equations, etc., matrix decomposition will be used. However, QR decomposition is one of the most widely used matrix decomposition methods in channel identification, recursive system identification, nonlinear system fuzzy identification, etc. However, the calculation of QR decomposition is relatively complicated. There are three commonly used algorithms at present, namely It is: Schmidt orthogonalization, matrix elementary transformation, using Givens (Givens) transformation, for a randomly generated non-singular square matrix A, as the size of the square matrix increases, the time required for decomposition increases very rapidly, so when After the scale of the matrix reaches a certain level, the personal computing power cannot meet the requirements, and only external computing resources can be borrowed

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