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Privacy-Preserving Similarity-Based Text Retrieval

机译:基于隐私保护的相似度文本检索

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摘要

Users of online services are increasingly wary that their activities could disclose confidential information on their business or personal activities. It would be desirable for an online document service to perform text retrieval for users, while protecting the privacy of their activities. In this article, we introduce a privacy-preserving, similarity-based text retrieval scheme that (a) prevents the server from accurately reconstructing the term composition of queries and documents, and (b) anonymizes the search results from unauthorized observers. At the same time, our scheme preserves the relevance-ranking of the search server, and enables accounting of the number of documents that each user opens. The effectiveness of the scheme is verified empirically with two real text corpora.
机译:在线服务的用户越来越警惕他们的活动可能会泄露有关其业务或个人活动的机密信息。希望在线文档服务在保护用户活动隐私的同时为用户执行文本检索。在本文中,我们介绍了一种基于隐私的,基于相似度的文本检索方案,该方案(a)阻止服务器准确地重建查询和文档的术语组成,并且(b)使未经授权的观察者获得的搜索结果匿名化。同时,我们的方案保留了搜索服务器的相关性排名,并允许计算每个用户打开的文档数量。该方案的有效性通过两个真实文本语料库进行了经验验证。

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