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首页> 外文会议>IEEE International Conference on Power, Control, Signals and Instrumentation Engineering >Time efficient privacy-preserving multi-keyword ranked search over encrypted cloud data
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Time efficient privacy-preserving multi-keyword ranked search over encrypted cloud data

机译：在加密的云数据上高效地保护隐私的多关键字排名搜索

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摘要

Now days for protecting data privacy, sensitive data have to be encrypted before outsourcing, which helps in data utilization based on plaintext keyword search. Thus, enabling an encrypted cloud data search service is very important. Data owners are motivated to outsource their data management systems from local sites to the commercial public cloud for great flexibility and economic savings. Considering the large number of data users and documents in the cloud, it is necessary to allow multiple keywords in the search request and return documents in the order of their relevance to these keywords. In this paper, we define and solve the challenging problem of privacy-preserving multi-keyword ranked search over encrypted data in cloud computing. Among various multi-keyword semantics, we choose the efficient similarity measure of coordinate matching, i.e., as many matches as possible, to capture the relevance of data documents to the search query. We further use inner product similarity to quantitatively evaluate such similarity measure. We first propose a basic idea for the MRSE based on secure inner product computation to efficiently achieve multi-keyword ranked search and then give two significantly improved MRSE schemes to achieve various privacy requirements.
机译：为了保护数据隐私，如今已经有很多日子了，敏感数据必须在外包之前进行加密，这有助于基于明文关键字搜索的数据利用。因此，启用加密的云数据搜索服务非常重要。激励数据所有者将其数据管理系统从本地站点外包到商业公共云，以实现极大的灵活性和经济节省。考虑到云中大量的数据用户和文档，有必要在搜索请求中允许多个关键字并按与这些关键字相关的顺序返回文档。在本文中，我们定义并解决了云计算中对加密数据进行隐私保护的多关键字排名搜索这一具有挑战性的问题。在各种多关键字语义中，我们选择坐标匹配的有效相似性度量，即尽可能多的匹配，以捕获数据文档与搜索查询的相关性。我们进一步使用内积相似性来定量评估这种相似性度量。我们首先提出一个基于安全内积计算的MRSE的基本思想，以有效地实现多关键字排名搜索，然后给出两种显着改进的MRSE方案，以实现各种隐私要求。

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