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Privacy Preserving Unsupervised Clustering over Vertically Partitioned Data

机译:在垂直分区数据中保留无监督群集的隐私

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The exponential growth of databases containing personal information has rendered the task of extracting high quality information from collections of such databases very important. This task is hindered by the security concerns that arise, due to the confidentiality of the data records, and the reluctance of the organizations to disclose their data. This paper proposes a clustering algorithmic scheme that ensures privacy and confidentiality of the data without compromising the effectiveness of the clustering algorithm nor imposing high communication costs.
机译:包含个人信息的数据库的指数增长使得从这些数据库的集合中提取高质量信息的任务非常重要。由于数据记录的机密性以及组织不愿披露其数据,因此此任务受到出现的安全问题。本文提出了一种聚类算法方案,可确保数据的隐私和机密性,而不会影响聚类算法的有效性,也不估计高通信成本。

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