Privacy-preserving k-nn classification method based on vector homomorphic encryption

A technology of homomorphic encryption and privacy protection, applied in the field of vector classification, it can solve the problems of estimated density diffusion and heavy computational burden, and achieve the effect of accurate classification

Active Publication Date: 2020-03-31
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
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  • Claims
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Problems solved by technology

Due to the K-nearest neighbor rule for probability estimation density, there are two disadvantages. One is that the integral of the estimated density must be diffused to infinity, and the other is that there is a heavy burden on the calculation.

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  • Privacy-preserving k-nn classification method based on vector homomorphic encryption
  • Privacy-preserving k-nn classification method based on vector homomorphic encryption

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Embodiment Construction

[0039] like figure 1 Shown the present invention is based on the privacy protection K-NN classification method of vector homomorphic encryption, comprises steps:

[0040] A. Receive query vector set (x 1 , x 2 ,...x n ) and standard vector set (p 1 ,p 2 ,...p m ), where the standard vector set (p 1 ,p 2 ,...p m ) corresponds to a standard classification label (t 1 , t 2 ,...t m );

[0041] B. Due to the formula Sc=ωx+e, S is the key, c is the ciphertext, ω is a large integer, x is the query vector, e is the error term, and the value of each element of e is not greater than At the same time |S| (n represents the number of rows of the query matrix G, which is the number of vectors of the query vector group, and w represents the column number of the query matrix G, which is the dimension of each query vector), then it can be obtained: (GS)c=ωGx+Ge, It can be seen that Gx is encrypted into ciphertext c under the secret key GS. So by querying the set of vectors (x 1...

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Abstract

The present invention relates to a privacy protection K-NN classification method based on vector homomorphic encryption, comprising: A. receiving a query vector group and a standard vector group; B. generating a matrix G through the query vector group, and the standard vector group uses the key S through State encryption to generate a ciphertext group and a new key GS; C. Convert the new key GS to a conversion key S' to obtain the conversion matrix M and conversion ciphertext group at this time; D. Use the conversion key S 'Decrypt the converted ciphertext group to obtain the decrypted vector group; E. Attach classification labels to the corresponding query vectors according to the components of the K minimum decrypted vectors. The invention can protect the user's private data well, and when the private data is protected, the user's query vector can be efficiently and accurately classified through the K-NN algorithm, which improves the efficiency of vector type judgment and expands the vector The scope of application of type judgments.

Description

technical field [0001] The invention relates to a vector classification method of a K-NN algorithm under privacy protection, in particular to a privacy protection K-NN classification method based on vector homomorphic encryption. Background technique [0002] K-Nearest Algorithm (K-NN) is widely used in pattern recognition, and also has good performance in data classification. In the era of big data, complex calculations are often outsourced to third-party clouds. However, in this process, the outsourced data may contain some sensitive data of sensitive users. Directly outsourcing user information to a third-party cloud may cause some private information of users to leak out, causing losses to users. A direct solution is to encrypt the data and then upload it to the cloud, but this also brings some new challenges to the efficiency of the K-NN algorithm. Homomorphic encryption technology can operate under ciphertext, and the result after decryption is correct. Therefore, t...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L29/06H04L9/08H04L9/00G06F16/35G06K9/62
CPCH04L9/008H04L9/088H04L63/0407G06F16/35G06F18/24147
Inventor 杨浩淼何伟超黄云帆冉鹏姚铭轩金保隆
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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