Cuckoo-Filter Based Privacy-Aware Search over Encrypted Cloud Data

Qinghan Xue, M. Chuah
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引用次数: 6

Abstract

Many organizations and individual users are out-sourcing their information which includes sensitive data into the cloud. To deal with the potential risks of privacy exposure, such data is typically encrypted before being outsourced but users would like to conduct keyword-based searches. Traditional searchable encryption techniques are overly restrictive for they only allow exact keyword search. Thus, fuzzy keyword search is needed to deal with typos in users' search strings. In this paper, we present a Cuckoo Filter Based Private Keyword Search Scheme (CFPKS) to provide privacy-aware keyword search over encrypted data. This CFPKS scheme uses a bed-tree structure-based index to boost search efficiency, a wildcard approach to support fuzzy keyword search, and a Cuckoo-filter to improve search accuracy and storage efficiency. Our scheme handles both typos and query unlinkability. Using a large ACM publication dataset, the evaluation results comparing the search efficiency and accuracy of our proposed CFPKS scheme with three existing schemes show that our scheme achieves higher search accuracy with lower search cost.
基于杜鹃过滤器的加密云数据隐私感知搜索
许多组织和个人用户正在将包括敏感数据在内的信息外包到云中。为了应对隐私暴露的潜在风险,此类数据通常在外包之前进行加密,但用户希望进行基于关键字的搜索。传统的可搜索加密技术过于严格,因为它们只允许精确的关键字搜索。因此,需要模糊关键字搜索来处理用户搜索字符串中的拼写错误。在本文中,我们提出了一种基于布谷鸟过滤器的私有关键字搜索方案(CFPKS),以提供对加密数据的隐私感知关键字搜索。该CFPKS方案使用基于床树结构的索引来提高搜索效率,使用通配符来支持模糊关键字搜索,使用Cuckoo-filter来提高搜索精度和存储效率。我们的方案处理错别字和查询不可链接性。利用一个大型ACM出版物数据集,将所提出的CFPKS方案与现有的三种方案的搜索效率和准确性进行了比较,结果表明,该方案以较低的搜索成本获得了更高的搜索精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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