Motivated by privacy preservation requirements for outsourced data, keyword searches over encrypted cloud data have become a hot topic. Compared to single-keyword exact searches, multi-keyword fuzzy search schemes attract more attention because of their improvements in search accuracy, typo tolerance, and user experience in general. However, existing multi-keyword fuzzy search solutions are not sufficiently efficient when the file set in the cloud is large. To address this, we propose an Efficient Leakage-resilient Multi-keyword Fuzzy Search (EliMFS) framework over encrypted cloud data. In this framework, a novel two-stage index structure is exploited to ensure that search time is independent of file set size. The multi-keyword fuzzy search function is achieved through a delicate design based on the Gram Counting Order, the Bloom filter, and the Locality-Sensitive Hashing. Furthermore, considering the leakages caused by the two-stage index structure, we propose two specific schemes to resist these potential attacks in different threat models. Extensive analysis and experiments show that our schemes are highly efficient and leakage-resilient.

中文翻译：

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EliMFS：实现对加密云数据的高效、防泄漏和多关键字模糊搜索

受外包数据隐私保护要求的推动，加密云数据的关键词搜索成为热门话题。与单关键字精确搜索相比，多关键字模糊搜索方案因其在搜索准确性、错别字容忍度和总体用户体验方面的改进而受到更多关注。然而，现有的多关键字模糊搜索解决方案在云端文件集较大时效率不高。为了解决这个问题，我们提出了一种基于加密云数据的高效泄漏弹性多关键字模糊搜索（EliMFS）框架。在该框架中，利用了一种新颖的两阶段索引结构来确保搜索时间与文件集大小无关。多关键词模糊搜索功能是通过基于Gram Counting Order、Bloom filter、和局部敏感哈希。此外，考虑到由两阶段索引结构引起的泄漏，我们提出了两种具体的方案来抵御不同威胁模型中的这些潜在攻击。广泛的分析和实验表明，我们的方案高效且具有泄漏弹性。