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首页> 外文期刊>Journal of digital information management >Mining Frequent Patterns Through Microaggregation in Differential Privacy
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Mining Frequent Patterns Through Microaggregation in Differential Privacy

机译：通过微聚合在差分隐私中挖掘频繁模式

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摘要

Frequent pattern mining has been widely employed to analyze transaction datasets, but the question of how sensitive information contained in a dataset should be protected remains remains relatively unanswered. The differential privacy model provides a robust privacy guarantee, but the k-anonymity model provides better dataset utility. In this paper, a synergetic approach is proposed to simultaneously protect privacy and enhance data utility when mining top-k frequent patterns. First, microaggregated data is released, which achieves k-anonymity regardless of the query types the user may be using. Second, top-k frequent patterns are selected based on microaggregated data using the exponential mechanism. Finally, the true support of each top-k frequent pattern is perturbed by adding Laplace noise.
机译：频繁的模式挖掘已被广泛用于分析交易数据集，但是如何保护数据集中包含的敏感信息的问题仍然没有得到解答。差异隐私模型提供了可靠的隐私保证，但k匿名模型提供了更好的数据集实用程序。本文提出了一种协同方法，可以在挖掘前k个频繁模式时同时保护隐私并增强数据实用性。首先，释放微聚合的数据，无论用户使用哪种查询类型，都可以实现k匿名。其次，使用指数机制基于微聚集数据选择前k个频繁模式。最后，通过添加拉普拉斯噪声来扰乱每个前k个频繁模式的真正支持。

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  • 来源
    《Journal of digital information management》 |2015年第2期|126-131|共6页
  • 作者

    Z. NI; Q.M. LI; X.Q. LIU; T. LI; R. HOU;

  • 作者单位

    School of Computer Science and Engineering, Nanjing University of Science and Technology No.200 Xiaolingwei, 210094 Nanjing, China;

    School of Computer Science and Engineering, Nanjing University of Science and Technology No.200 Xiaolingwei, 210094 Nanjing, China;

    School of Computer Science and Engineering, Nanjing University of Science and Technology No.200 Xiaolingwei, 210094 Nanjing, China;

    School of Computer Science, Florida International University 11200 SW 8th Street, Miami, FL 33199, U.S.A.;

    Yahoo Center of Latin America, Blanco Encalada 2120, Santiago, Chile;

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

    Frequent Pattern; Differential Privacy; K-Anonymity; Microaggregation;

    机译：频繁模式差异隐私;K-匿名;微聚集;

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