DP-PartFIM: Frequent Itemset Mining Using Differential Privacy and Partition

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Xinyu Liu;Wensheng Gan;Lele Yu;Yining Liu
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引用次数: 0

Abstract

Itemset mining is a popular data mining technique for extracting interesting and valuable information from large datasets. However, since datasets contain sensitive private data, it is not permitted to directly mine the data or share the mining results. Previous privacy-preserving frequent itemset mining research was not efficient because of the use of privacy budgets or long transaction truncation strategies, which are impractical for large datasets. In this article, we propose a more efficient partition mining technology, DP-PartFIM, based on differential privacy, which protects privacy while mining data. DP-PartFIM uses partition mining to mine frequent itemsets and constructs vertical data storage formats for each partition, which makes the algorithm equally efficient for large datasets. To protect data privacy, DP-PartFIM adds Laplace noise to support candidate itemsets. The experimental results show that, compared with the classical privacy-preserving itemset mining methods, DP-PartFIM better guarantees data utility and privacy.
DP-PartFIM:利用差异隐私和分区挖掘常项集
项目集挖掘是一种流行的数据挖掘技术,用于从大型数据集中提取有趣和有价值的信息。但是,由于数据集包含敏感的私有数据,因此不允许直接挖掘数据或共享挖掘结果。以往的保护隐私的频繁项集挖掘研究由于使用隐私预算或长事务截断策略而效率不高,这对于大数据集是不切实际的。在本文中,我们提出了一种更高效的基于差分隐私的分区挖掘技术DP-PartFIM,它在挖掘数据的同时保护了隐私。DP-PartFIM利用分区挖掘挖掘频繁项集,并为每个分区构建垂直的数据存储格式,使得算法对大型数据集同样高效。为了保护数据隐私,DP-PartFIM增加了拉普拉斯噪声来支持候选项集。实验结果表明,与传统的保护隐私的项集挖掘方法相比,DP-PartFIM能更好地保证数据的实用性和隐私性。
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来源期刊
IEEE Transactions on Emerging Topics in Computing
IEEE Transactions on Emerging Topics in Computing Computer Science-Computer Science (miscellaneous)
CiteScore
12.10
自引率
5.10%
发文量
113
期刊介绍: IEEE Transactions on Emerging Topics in Computing publishes papers on emerging aspects of computer science, computing technology, and computing applications not currently covered by other IEEE Computer Society Transactions. Some examples of emerging topics in computing include: IT for Green, Synthetic and organic computing structures and systems, Advanced analytics, Social/occupational computing, Location-based/client computer systems, Morphic computer design, Electronic game systems, & Health-care IT.
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