• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering >Differential Privacy Information Publishing Algorithm based on Cluster Anonymity
【24h】

Differential Privacy Information Publishing Algorithm based on Cluster Anonymity

机译：基于聚类匿名性的差分隐私信息发布算法

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

With the development of Internet technology, the attacker gets more and more complex background knowledge, which makes the anonymous model susceptible to background attack. Although the differential privacy model can resist the background attack, it reduces the versatility of the data. In this paper, this paper proposes a differential privacy information publishing algorithm based on clustering anonymity. The algorithm uses the cluster anonymous algorithm based on KD tree to cluster the original data sets and gets anonymous tables by anonymous operation. Finally, the algorithm adds noise to the anonymous table to satisfy the definition of differential privacy. The algorithm is compared with the DCMDP (Density-Based Clustering Mechanism with Differential Privacy, DCMDP) algorithm under different privacy budgets. The experiments show that as the privacy budget increases, the algorithm reduces the information loss by about 80% of the published data.
机译：随着Internet技术的发展，攻击者获得了越来越复杂的背景知识，这使得匿名模型容易受到背景攻击。尽管差分隐私模型可以抵抗后台攻击，但它降低了数据的多功能性。本文提出了一种基于聚类匿名性的差分隐私信息发布算法。该算法使用基于KD树的聚类匿名算法对原始数据集进行聚类，并通过匿名操作获取匿名表。最后，该算法将噪声添加到匿名表中，以满足差分隐私的定义。在不同的隐私预算下，将该算法与DCMDP（基于差分的基于密度的聚类机制，DCMDP）算法进行了比较。实验表明，随着隐私预算的增加，该算法减少了约80％的已发布数据的信息丢失。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号