Xianglai Yang, Jianmei Cao, Youjie Wang
As cloud computing becomes widespread, more and more users prefer to outsource their local sensitive data into the cloud. In order to protect data privacy, these sensitive data usually have to be encrypted before outsourcing, which makes effective data utilization a very difficult task. Although traditional searchable encryption techniques allow users to securely search over encrypted cloud data, they only support exact single keyword search, i.e. they do not allow any minor spelling errors or format inconsistencies. Besides, these traditional schemes only support Boolean search, without capturing any relevance of data files and rarely sort the search result. Recently, fuzzy keyword search over encrypted data techniques are introduced to resolve the problem of spelling errors and format inconsistencies. But these methods may incur large index size, search result inaccuracy and high search complexity, which greatly reduce the system usability and efficiency. This paper proposes the solution for privacy preserving ranked fuzzy keyword search over encrypted cloud data with small index. K-grams and Jaccard coefficient are utilized to construct fuzzy keyword set and produce fuzzy results, and an efficient relevance criteria is also provided to capture the relevance between data files and search requests. Extensive experimental results show the efficiency of our proposed method.
K-Gram, Fuzzy Keyword Search, Ranked Keyword Search, Searchable Encryption, Cloud Computin
Xianglai Yang, Jianmei Cao, Youjie Wang, An Efficient and Privacy-preserving Ranked Fuzzy Keywords Search over Encrypted Cloud Data. 2019 International Computer Science and Applications Conference (ICSAC 2019). 2019: 17-22.