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A Privacy Protection Method for Multipart Graphs Based on Multidimensional Sensitive Data Publishing

A sensitive data, privacy protection technology, applied in the field of privacy protection, can solve problems such as excessive information loss and privacy leakage

Active Publication Date: 2019-04-26
GUANGXI NORMAL UNIV
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Problems solved by technology

[0008] The technical problem to be solved by the present invention is aimed at the release of multi-dimensional sensitive data. Existing privacy protection methods have problems such as excessive information loss and correlation between multiple sensitive attributes that lead to privacy leakage. A method based on multi-dimensional sensitive data release is provided. Multipart graph privacy protection method

Method used

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  • A Privacy Protection Method for Multipart Graphs Based on Multidimensional Sensitive Data Publishing
  • A Privacy Protection Method for Multipart Graphs Based on Multidimensional Sensitive Data Publishing
  • A Privacy Protection Method for Multipart Graphs Based on Multidimensional Sensitive Data Publishing

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Embodiment Construction

[0035] The present invention provides a multipart graph-based privacy protection method for the release of multi-sensitive attribute data, which mainly includes two parts: constructing the original table data into a multipart graph form and a privacy protection strategy based on the multipart graph.

[0036] 1. Construct the original table data into a multipart graph form. like figure 1 As shown, the Name column in the original dataset is ID, Age, Zip, and Sex are non-sensitive attributes, and Salary, Marital Status, and Disease are sensitive attributes. When constructing a multipartite graph, an undirected graph G(V m , E, W) abstractly represent multi-sensitive attribute datasets, V m is a finite set of vertices (where V 1 is the set of user nodes with quasi-identifier labels, V i Indicates the node set of the i-1th sensitive attribute in the data set), E is a binary relationship on V indicating the relationship between different node sets, that is, a certain user has a ...

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Abstract

The invention discloses a multi-partite graph privacy protection method published based on multi-dimension sensitive data. The multi-partite graph privacy protection method mainly comprises the steps of constructing original table data into two large parts of a multi-partite graph form and a privacy protection strategy based on the multi-partite graph. An ID is adopted as one type of nodes, a quasi-identity sign corresponding to the ID is expressed through a form of label, each sensitive property is shown through the adoption of one type of the nodes, and if one user has a certain sensitive property, one side must exit between two nodes so as to show the relevance. A clustering method is adopted to conduct grouping, users in the same group are regarded as one super node, the relevance degree between the sensitive properties and the users is shown through a side with weight, the weight of the side is the probability that the group of the users have the sensitive properties, the relevance degree among the properties is also shown through the side with weight, so that the purposes that the relevance among the properties is preserved, and multiple sensitive properties and correlated privacy security thereof are effectively protected are achieved.

Description

technical field [0001] The invention relates to the technical field of privacy protection, in particular to a multi-part image privacy protection method based on multi-dimensional sensitive data release. Background technique [0002] Today's human society has entered the information age, and the information industry is also showing a trend of rapid development. The Internet continues to penetrate into various fields such as politics, economy, culture, medical care, and education, and generates a large amount of data, which can be used for massive data analysis when shared and published. With the increasing development and wide application of data mining technology, these data releases have brought great convenience to people in scientific research, group behavior trend analysis, disease prediction, business decision-making and public opinion monitoring, and have great social and social significance. Economic Value. As an effective means of data sharing, data publishing tec...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F21/62
CPCG06F21/6245
Inventor 王利娥李先贤郭亚萌
Owner GUANGXI NORMAL UNIV
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