  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
end of 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 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 for both accuracy and efficiency. Moreover, these
designs will not hurt the fundamental security. Experiments on a real-world
dataset demonstrate the effectiveness of our proposed approach, which
outperforms currently popular approaches focusing on similar tasks.
