Privacy protection k-means clustering method and device, medium and terminal

A clustering method and privacy protection technology, which is applied in the field of privacy protection, can solve problems such as high time cost, inaccurate clustering results, and insufficient security, and achieve low calculation cost and time cost, saving calculation time cost, and efficient privacy The effect of protection

Pending Publication Date: 2021-11-09
XIDIAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Through the above analysis, the problems and defects of the existing technology are: the existing clustering method makes the user's private information and classification results extremely easy to

Method used

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  • Privacy protection k-means clustering method and device, medium and terminal
  • Privacy protection k-means clustering method and device, medium and terminal
  • Privacy protection k-means clustering method and device, medium and terminal

Examples

Experimental program
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Example Embodiment

[0093] Example 1:

[0094] A kind of efficient privacy protection k-means clustering algorithm that the example of the present invention provides comprises the following steps:

[0095] 1. Data collection: the data owner (user) uses the server S 1 public key pk 1 Encrypted data sent to server S 0 , and then use server S 0 public key pk 0 Encrypted data sent to server S 1 .

[0096] 2. Similarity measurement: server S 0 Send the ciphertext central point information to the server S 1 , server S 1 Decrypt and use homomorphic technology to calculate the ciphertext distance from each point to the center point, and send the ciphertext distance to the server S 0 .

[0097] 3. Compare update iteration: server S 0 Decrypt and reclassify according to the distance comparison results to get a new round of classification results, repeat the second and third steps until the classification results do not change.

[0098] 4. Compare update iteration: server S 0 Received from serv...

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Abstract

The invention belongs to the technical field of information privacy protection, and discloses a privacy protection k-means clustering method and device, a medium and a terminal, and the privacy protection k-means clustering method comprises the steps: collecting and encrypting original data, processing the encrypted data through two servers, classifying the data through a K-means algorithm, and grouping similar data according to distance measurement. The privacy information of the user can be well protected in the whole K-means clustering algorithm process, and the invention is friendly in the two aspects of calculation cost and time. The privacy protection k-means clustering method provided by the invention has the advantages of user friendliness, safety, correctness and effectiveness. The efficient privacy protection k-means clustering method provided by the invention is efficient and has linear complexity, and correctness and efficiency are not affected under the condition that the problem of user information privacy is solved.

Description

technical field [0001] The invention belongs to the technical field of privacy protection, and in particular relates to a privacy protection k-means clustering method, equipment, medium and terminal. Background technique [0002] Present: Machine learning is an important and a hot topic in the current environment. The advent of machine learning has led to breakthroughs in solving numerous problems in various fields, such as recommendation services, spam filtering, web search engines, fraud detection, stock market analysis, and authentication technology. While recent technologies enable more efficient storage and computation of big data, protecting the combined data from different sources remains a major challenge. [0003] However, machine learning needs to rely on a large amount of training data. The original data is messy. At the same time, the existing K-means algorithm is used to classify the data directly in the plaintext, which will lead to the leakage of the user's p...

Claims

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Application Information

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IPC IPC(8): G06F21/62G06K9/62
CPCG06F21/6245G06F18/23213
Inventor 刘雪峰张思君雷静
Owner XIDIAN UNIV
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