An Efficient Privacy Preserving Computation of Multiset Intersection Cardinality

Harmanjeet Kaur, Neeraj Kumar, J. Rodrigues
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引用次数: 0

Abstract

The multi-set intersection cardinality operation is used for calculation of similarity between two sets which has various applications such as cluster analysis, image segmentation, social network analysis, etc. The need of Privacy Preserving Computation of Multi-set Intersection Cardinality (PPCMIC) operation is raised when two parties want to compute similarities between their datasets without disclosing their data to each other. Existing methods for PPCMIC are either insecure or inefficient. In our work, to address this gap, PPCMIC protocol based on lightweight randomization protocol is proposed which is secure and efficient in terms of computation cost. The experimental work has been done on simulated and real datasets to show that proposed protocols are more efficient then the existing techniques.
一种高效的多集相交基数隐私保护计算方法
多集交集基数运算用于计算两集之间的相似度,在聚类分析、图像分割、社会网络分析等领域有广泛的应用。当双方希望在不向对方泄露数据的情况下计算数据集之间的相似度时,提出了多集相交基数运算的隐私保护计算需求。现有的PPCMIC方法要么不安全,要么效率低下。在我们的工作中,为了解决这一差距,提出了基于轻量级随机化协议的PPCMIC协议,该协议在计算成本方面安全高效。在模拟和实际数据集上进行了实验,结果表明所提出的协议比现有技术更有效。
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