Better Accuracy and Efficiency for Fuzzy Search on Encrypted Data
release_gwp2izzo3zflpj6wgvrplybhoe
by
Jinkun Cao, Jinhao Zhu, Liwei Lin, Ruhui Ma, Haibing Guan
2019
Abstract
As cloud computing becomes prevalent in recent years, more and more
enterprises and individuals outsource their data to cloud servers. To avoid
privacy leaks, outsourced data usually is encrypted before being sent to cloud
servers, which disables traditional search schemes for plain text. To meet both
ends, security, and searchability, search-supported encryption is proposed.
However, many previous schemes suffer severe vulnerability when typos and
semantic diversity exist in query requests. To overcome such flaw, higher
error-tolerance is always expected for search-supported encryption design,
sometimes defined as 'fuzzy search'. In this paper, we propose a new scheme of
multi-keyword fuzzy search scheme over encrypted and outsourced data. Our
approach introduces a new mechanism to map a natural language expression into a
word-vector space. Compared with previous approaches, our design shows higher
robustness when multiple kinds of typos are involved. Besides, our approach is
enhanced with novel data structures to improve search efficiency. These two
innovations can work well contributing to accuracy and efficiency respectively.
And these designs will not hurt the fundamental security. Experiments on
real-world dataset demonstrate the effectiveness of our proposed approach,
which outperforms currently popular approaches focusing on similar tasks as
well.
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