Abstract
Data mining has been a popular research area for more than a decade. There are several problems associated with data mining. Among them clustering is one of the most interesting problems. However, this problem becomes more challenging when dataset is distributed between different parties and they do not want to share their data. So, in this paper we propose a privacy preserving two party hierarchical clustering algorithm vertically partitioned data set. Each site only learns the final cluster centers, but nothing about the individual’s data.
Highlights
Data Mining has been a popular research area for more than a decade because of its ability of efficiently extracting statistics and trends from large sets of data
In this paper we propose a privacy preserving two party hierarchical clustering algorithm vertically partitioned data set
In this paper a novel approach of hierarchical clustering is given for vertically partitioned data set which considers the privacy factor
Summary
Data Mining has been a popular research area for more than a decade because of its ability of efficiently extracting statistics and trends from large sets of data. When dealing with such sensitive information, the privacy issues become major concerns, as any leakage or compromise of data may result in potential harm to individuals or financial losses to the corporate. It is widely used in the applications of financial affairs, marketing, insurance, medicine, chemistry, machine learning, data mining, etc. Most of the clustering problem solutions for vertically partitioned data set are based on k-means algorithm. In this paper a secure hierarchical clustering approach over vertically partitioned data is provided This hierarchical clustering is more efficient than k-means clustering algorithm in identifying cluster centers.
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