Secure Association Rule Mining on Vertically Partitioned Data Using Private-Set Intersection
Secure Association Rule Mining on Vertically Partitioned Data Using Private-Set Intersection
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DOI：
10.1109/access.2020.3014330
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Kenta Nomura;Yoshiaki Shiraishi;M. Mohri;M. Morii
中科院分区：
文献类型：
--
作者：
Kenta Nomura;Yoshiaki Shiraishi;M. Mohri;M. Morii
数据挖掘需要从大型无组织数据集中发现意想不到但可重用的知识。在众多的数据挖掘算法中，关联规则挖掘（ARM）是最常见的一种。它的开发是为了将所有数据聚合到一个站点，然后对其进行挖掘。近年来，不同领域的组织需要合作创造新的价值。然而，组织之间和组织内部的数据挖掘引起了隐私和保密问题。在我们的方案中，除了记录的数量，各方不能共享任何东西，包括候选项集。这项研究的重点是私有集的交集，而不是标量积，并表明这种交集使组织能够在垂直分区的数据上执行ARM，允许灵活的信息共享，同时保护隐私，而不增加通信和计算成本。此外，我们专注于这样一个事实，即各方之间的协议回合数可以减少，并提出了三个用例，其中所提出的方案比现有方案更有效。
Data mining entails the discovery of unexpected but reusable knowledge from large unorganized datasets. Among the many available data-mining algorithms, association rule mining (ARM) is very common. It was developed to aggregate all data into one site and subsequently mine them. In recent years, organizations in different fields have been required to collaborate to create new value. However, data mining among and within organizations has raised privacy and confidentiality concerns. In our scheme, parties cannot share anything other than the number of records, including the candidate itemset. This study focuses on the private-set intersection instead of the scalar product and shows that this intersection enables organizations to execute ARM on vertically partitioned data, allowing flexible information sharing while preserving privacy without increasing communication and computation costs. Furthermore, we focus on the fact that the number of protocol rounds among parties can be reduced and present three use cases in which the proposed scheme works more effectively than the existing schemes.