Differential privacy protection method for online social network based on stratified random graph

A layered random graph and differential privacy technology, applied to electrical components, transmission systems, etc., can solve the problems of not strictly defining the attack model, and the attacker's background knowledge cannot be quantitatively analyzed, etc.

Inactive Publication Date: 2016-03-02
NAT UNIV OF DEFENSE TECH
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Problems solved by technology

The disadvantage of these privacy protection methods is that the attack model is not str...

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  • Differential privacy protection method for online social network based on stratified random graph
  • Differential privacy protection method for online social network based on stratified random graph
  • Differential privacy protection method for online social network based on stratified random graph

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Embodiment Construction

[0064] Such as figure 1 As shown, the steps of the online social network differential privacy protection method based on hierarchical random graph in this embodiment include:

[0065] 1) input network G;

[0066] 2) Construct the tree structure T of the network G based on the hierarchical random graph model;

[0067] 3) According to the preset privacy budget ε 1 , the sampling tree T is obtained by sampling in the tree structure T of the network G by the Markov Monte Carlo method sample ;

[0068] 4) Take the sampling tree T sample the root node R root as the initial current node;

[0069] 5) According to the preset privacy budget ε 2 , calculate the associated probability value of the current node {P r};

[0070] 6) Find a group of node pairs with the current node as the nearest parent node in the network G, and associate the probability value {P r} set an edge between the set of node pairs;

[0071] 7) Judging the sampling tree T sample Whether the traversal is c...

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Abstract

The invention discloses a differential privacy protection method for an online social network based on a stratified random graph. The differential privacy protection method comprises the following steps: inputting a network; constructing a tree structure of the network based on a stratified random graph model; sampling in the network through a Markov chain Monte Carlo method according to a preset privacy budget so as to obtain a sampled tree; taking the root node of the sampled tree as an initial current node; calculating an associated probability value of the current node according to the preset privacy budget; finding out a set of node pairs by taking the current node as the nearest father node in the network, and setting an edge among the set of node pairs according to the associated probability value; judging whether traversal of the sampled tree is completed or not, and if not, continuously traversing the next node in the sampled tree; and otherwise, outputting a purified network composed of edges arranged among all the sets of nodes and nodes thereof. According to the invention, the privacy protection problem of sensitive structural data information in the social network can be solved; differential privacy protection requirements can be satisfied; and simultaneously, the good data availability is kept.

Description

technical field [0001] The invention relates to a sensitive information protection technology for the key structure of an online social network, in particular to an online social network differential privacy protection method based on a layered random graph. Background technique [0002] With the development of Internet technology and the popularity of online social network services, data sharing has become more and more convenient, which has aroused people's concerns about their own privacy leakage. In recent years, social panic caused by data leakage has occurred frequently at home and abroad. For example, the famous American Internet company America Online (AOL) leaked a large number of users' web search records. Some people found out the real identity of the corresponding users based on these search records. The Internet habits of a large number of registered users were accidentally exposed. It can be seen from such incidents that protecting personal privacy is far more...

Claims

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Application Information

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IPC IPC(8): H04L29/06
CPCH04L63/0407H04L63/0428H04L63/08
Inventor 朱培栋陈亮王可蔡开裕刘小雪郑倩冰马迪杜秀春康文杰尚博文刘磊胡照明
Owner NAT UNIV OF DEFENSE TECH
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