Abstract-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 wildcard based approach to provide fuzzy keyword search. They also propose a solution for multikeyword 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 bedtree 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 wildcard approach for multi-keyword queries where many encrypted files are returned using single-word queries for approaches that do not support multi-keyword queries.
Chuah proposed a fuzzy multi-keyword search scheme, where edit distance is also used to evaluate the similarity between terms REF .
Privacy-Aware BedTree Based Solution for Fuzzy Multi-keyword Search over Encrypted Data
2011 31st International Conference on Distributed Computing Systems Workshops
2011 31st International Conference on Distributed Computing Systems Workshops
Computer Science