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首页> 外文期刊>Journal of Software Maintenance and Evolution >An improved anonymity model for big data security based on clustering algorithm
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An improved anonymity model for big data security based on clustering algorithm

机译：一种改进的基于聚类算法的大数据安全匿名模型

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摘要

The accumulation of massive data generates the new concept of big data. The relationships hidden in big data can bring great benefits, which have attracted public attentions. Meanwhile, the challenges of big data security are also more serious than ever. Privacy disclosure is one of the most concerned problems, and the privacy protection of big data is more difficult than traditional information protection. The technology of data publishing anonymous protection can provide privacy protection with the respect of data releasing. K-anonymity and L-diversity are two kinds of anonymity model. Their main idea is to generalize the value of quasi-identifier and make the data accord with the model. In this paper, we propose the improved model which integrate K-anonymity with L-diversity and can solve the problem of imbalanced sensitive attribute distribution. K-member clustering algorithm can translate the problem of anonymity into the problem of clustering and find a set of equivalence classes in which the records will be generalized to the same value. We utilize K-member clustering algorithm to realize the improved anonymity model which can reduce the algorithm execution time and information loss. The integration of anonymity model and clustering algorithm makes the generalization process more efficient, which is particularly important for big data. Copyright.
机译：海量数据的积累产生了大数据的新概念。大数据中隐藏的关系可以带来巨大的好处，引起了公众的关注。同时，大数据安全性的挑战也比以往更加严重。隐私公开是最令人关注的问题之一，大数据的隐私保护比传统的信息保护更加困难。数据发布匿名保护技术可以在数据发布方面提供隐私保护。 K-匿名和L-多样性是两种匿名模型。他们的主要思想是推广准标识符的值并使数据与模型相符。本文提出了一种改进的模型，该模型将K-匿名性与L-多样性相结合，可以解决敏感属性分布不平衡的问题。 K成员聚类算法可以将匿名问题转化为聚类问题，并找到一组等价类，在这些类中，记录将被概括为相同的值。我们利用K成员聚类算法来实现改进的匿名模型，该模型可以减少算法的执行时间和信息丢失。匿名模型和聚类算法的集成使泛化过程更加高效，这对于大数据尤为重要。版权。

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  • 作者

    Chunyong Yin; Sun Zhang; Jinwen Xi; Jin Wang;

  • 作者单位

    School of Computer and Software, Jiangsu Engineering Center of Network Monitoring, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China;

    School of Computer and Software, Jiangsu Engineering Center of Network Monitoring, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China;

    School of Computer and Software, Jiangsu Engineering Center of Network Monitoring, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China;

    College of Information Engineering, Yangzhou University, Yangzhou, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    big data; privacy protection; data publishing; K-anonymity; L-diversity; clustering;

    机译：大数据;隐私保护;数据发布;K-匿名性;L多样性聚类;

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