Secure ranked fuzzy multi-keyword search over outsourced encrypted cloud data

As the power of cloud computing became prevalent in the recent years, more and more sensitive information is being now shifted to the cloud. The cloud is a computing service that charges the user according to usage of the computing resources. This pay-as-you-use feature is the hallmark in cloud computing. Security of data is a matter of concern and especially when cloud storage service is used. Encryption is the most effective way to achieve data security. Sensitive data have to be encrypted before outsourcing in spite of the fact that, retrieval of encrypted data becomes an intriguing task. Although various searching techniques are used for retrieving the encrypted cloud data through keywords and these techniques retrieve the files in a ranked order but they either support rank based single keyword search or multi-keyword search with static keyword dictionary. There is a greater overhead in updating the index file or the keyword dictionary when new files need to be uploaded. Also, efficient data discovery and user searching experience needs to be enhanced. In this paper, for the first time we formulize and solve the problem of effective Secure ranked Fuzzy multi-keyword search over outsourced encrypted cloud data (RFMS). RFMS enhances user searching experience by returning the matching files when user´s input query either exactly matches the predefined keyword dictionary or closest possible keywords in the dictionary based on similarity semantics when exact match fails. Information discovery has been made efficient by searching with multiple keywords with ranking so as to eliminate false positives. Keyword dictionary has been made dynamic. Overhead of updating the dictionary when new files need to be uploaded has been minimized. Also, by using one-to-many mapping between plaintext and cipher text, the method guarantees security.