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Two-Party Privacy-Preserving Agglomerative Document Clustering

机译:两方隐私保护聚集文档聚类

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摘要

Document clustering is a powerful data mining technique to analyze the large amount of documents and structure large sets of text or hypertext documents. Many organizations or companies want to share their documents in a similar theme to get the joint benefits. However, it also brings the problem of sensitive information leakage without consideration of privacy. In this paper, we propose a cryptography-based framework to do the privacy-preserving document clustering among the users under the distributed environment: two parties, each having his private documents, want to collaboratively execute agglomerative document clustering without disclosing their private contents.
机译:文档聚类是一种强大的数据挖掘技术,可以分析大量文档并构造大量文本或超文本文档。许多组织或公司希望以相似的主题共享他们的文档,以获得共同的利益。然而,这也带来了敏感信息泄漏而不考虑隐私的问题。在本文中,我们提出了一个基于密码学的框架来在分布式环境下在用户之间进行隐私保护文档的聚类:每个都有自己的私人文档的两方都希望在不公开其私人内容的情况下共同执行聚集文档的聚类。

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