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K-prototypes clustering data privacy protection method based on local differential privacy

A differential privacy and data privacy technology, applied in digital data protection, electronic digital data processing, instruments, etc., can solve problems such as amplifying noise

Pending Publication Date: 2022-02-01
HUNAN UNIV OF SCI & TECH
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, although local differential privacy can effectively deal with the third-party privacy leakage problem, there are still challenges in applying it to protect user data privacy in cluster analysis, that is, how to reduce the influence of noise in the process of cluster update centroid
If you directly calculate which centroid is closer to the user data based on the collected disturbance data, and use this to cluster the users, and then calculate the centroid of each cluster based on the disturbance data, the impact of noise will be further amplified

Method used

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  • K-prototypes clustering data privacy protection method based on local differential privacy
  • K-prototypes clustering data privacy protection method based on local differential privacy

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Embodiment Construction

[0049] The present invention will be further described below in conjunction with accompanying drawings and examples.

[0050] Such as figure 1 As shown, a K-prototypes clustering data privacy protection method based on local differential privacy includes the following steps:

[0051] Step 1: The user disturbs the data at the local end;

[0052] Step 1.1: Use local differential privacy technology to perturb user data to generate perturbed data;

[0053] Step 1.2: The user sends the disturbance data generated in step 1.1 to the server, and enters step 2;

[0054] The local terminal is a terminal under the control of the user, which stores the user's data, and these data need to be transmitted to the server for clustering;

[0055] The server is a platform that provides cluster analysis services for performing cluster analysis on the collected user data;

[0056] The type of the user data is dimensional mixed data, including both categorical data and numerical data. The serv...

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Abstract

The invention provides a K-prototypes clustering data privacy protection method based on local differential privacy, and the method comprises the steps: firstly carrying out the disturbance of mixed data at a user side through employing a local differential privacy technology, guaranteeing that a server side cannot obtain the real data of a user, and then completing the K-prototypes clustering through the interaction iteration of the server side and the user side. According to the method, third-party protection of user data privacy in the K-prototypes clustering process is achieved, and the method is simple in implementation process and easy to operate.

Description

technical field [0001] The invention belongs to the field of computer science and technology, in particular to a K-prototypes clustering data privacy protection method based on local differential privacy. Background technique [0002] Clustering is a commonly used data analysis method, which divides the data set into different clusters according to a certain standard, so that the data similarity in the same cluster is higher. K-means is a classic algorithm in clustering. Its implementation is simple and clustering is efficient, but it is only suitable for processing numerical data sets. K-modes algorithm is usually used for clustering of classified data. Most of the data in practical applications contains both numerical data and categorical data. For this, the K-prototypes algorithm combining K-means and K-modes algorithms is usually used. At present, cluster analysis plays an important role in many fields such as data mining and service recommendation. For example, throug...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F21/62G06F21/64G06K9/62
CPCG06F21/6245G06F21/64G06F18/23
Inventor 张少波原刘杰朱更明
Owner HUNAN UNIV OF SCI & TECH