Small business-oriented index construction of cloud data
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part II
F5P5: keyword search over encrypted data with five functions and five privacy assurances
ICICS'12 Proceedings of the 14th international conference on Information and Communications Security
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As Cloud Computing technology becomes more mature, many organizations and individuals are interested in storing more sensitive data e.g. personal health records, customers related information in the cloud. Such sensitive data needs to be encrypted before it is outsourced to the cloud. Typically, the cloud servers also need to support a keyword search feature for these encrypted files. Traditional searchable encryption schemes typically only support exact keyword matches. However, users sometimes have typos or use slightly different formats e.g. "data-mining" versus "data mining". Thus, fuzzy keyword search is a useful feature to have. Recently, some researchers propose using wild card based approach to provide fuzzy keyword search. They also propose a solution for multi-keyword search. Their approaches have some limitations, namely (a) their fuzzy keyword search solution consumes large storage size since it inserts every fuzzy keyword as a leaf node in the index tree, (b) their fuzzy single-keyword search solution does not support multi-keyword search, (c) the existing multi-keyword search scheme does not provide efficient incremental updates. In this paper, we propose a privacy-aware bed tree based approach to support fuzzy multi-keyword feature. Incremental updates can be easily done using our solution. We have implemented our solution. Our evaluation results show that our approach is more cost-effective in terms of storage size and construction time. Our search time is usually better than the wild card approach for multi-keyword queries where many encrypted files are returned using single-word queries for approaches that do not support multi-keyword queries.