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摘要:The application of social network collects a large amount of user data and sensitive data,which may reveal potential privacy information through analysis.At present,the differential privacy protection model gives a rigorous and quantitative representation and proof of privacy disclosure risk,which greatly guarantees the availability of data.Recently,differential privacy is very popular.However,differential privacy assumes that data sets are independent.In real life,few data sets are completely independent.In social networks,nodes have edges that are related.This paper proposes a solution to use differential privacy on correlation social network data and designs a mechanism for correlation social network data publishing.Reduce the large amount of noise added when the association graph data is published with differential privacy.Considering the degree of correlation between nodes in the graph data,this paper first proposed the degree of association between nodes and calculated the degree of association between each node to calculate the sensitivity of association.The correlation sensitivity is used to determine the noise level in the implementation of differential privacy.Then the hierarchical random graph model is used to add noise satisfying differential privacy to edge connection probability to generate the pending layout.Finally,the feasibility and effectiveness of the method are verified by the statistical analysis of degree distribution,aggregation coefficient and onedimensional structural entropy.
会议名称:

2019 The 9th International Workshop on Computer Science and Engineering (WCSE 2019)

会议时间:

2019-06-15

会议地点:

中国香港

  • 专辑:

    信息科技

  • 专题:

    计算机软件及计算机应用

  • DOI:

    10.26914/c.cnkihy.2019.038287

  • 分类号:

    TP309

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