...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Knowledge and Data Engineering, IEEE Transactions on >Differentially Private Frequent Itemset Mining via Transaction Splitting
【24h】

Differentially Private Frequent Itemset Mining via Transaction Splitting

机译：通过事务拆分的差分私有频繁项集挖掘

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Recently, there has been a growing interest in designing differentially private data mining algorithms. Frequent itemset mining (FIM) is one of the most fundamental problems in data mining. In this paper, we explore the possibility of designing a differentially private FIM algorithm which can not only achieve high data utility and a high degree of privacy, but also offer high time efficiency. To this end, we propose a differentially private FIM algorithm based on the FP-growth algorithm, which is referred to as PFP-growth. The PFP-growth algorithm consists of a preprocessing phase and a mining phase. In the preprocessing phase, to improve the utility and privacy tradeoff, a novel smart splitting method is proposed to transform the database. For a given database, the preprocessing phase needs to be performed only once. In the mining phase, to offset the information loss caused by transaction splitting, we devise a run-time estimation method to estimate the actual support of itemsets in the original database. In addition, by leveraging the downward closure property, we put forward a dynamic reduction method to dynamically reduce the amount of noise added to guarantee privacy during the mining process. Through formal privacy analysis, we show that our PFP-growth algorithm is -differentially private. Extensive experiments on real datasets illustrate that our PFP-growth algorithm substantially outperforms the state-of-the-art techniques.
机译：最近，对设计差异私有数据挖掘算法的兴趣日益浓厚。频繁项集挖掘（FIM）是数据挖掘中最基本的问题之一。在本文中，我们探索了设计差分私有FIM算法的可能性，该算法不仅可以实现高数据实用性和高度保密性，而且还可以提供较高的时间效率。为此，我们提出了一种基于FP-growth算法的差分私有FIM算法，称为PFP-growth。 PFP增长算法包括预处理阶段和挖掘阶段。在预处理阶段，为了提高实用性和隐私权衡，提出了一种新颖的智能拆分方法来转换数据库。对于给定的数据库，预处理阶段仅需要执行一次。在挖掘阶段，为了弥补因事务拆分而导致的信息损失，我们设计了一种运行时估计方法来估计原始数据库中项目集的实际支持。此外，通过利用向下封闭的特性，我们提出了一种动态减少方法，可以动态减少添加的噪声量，从而在采矿过程中确保隐私。通过正式的隐私分析，我们证明了我们的PFP增长算法是差分私有的。在真实数据集上进行的大量实验表明，我们的PFP增长算法明显优于最新技术。

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