FSS-SDD: fuzzy-based semantic search for secure data discovery from outsourced cloud data

In current decade, cloud computing has become more prevalent and utilized for managing more sensitive data that are stored over cloud storage. While outsourcing the sensitive and private data on to the cloud, security is the major factor to be concentrated in efficient data search using keywords. As is auspicious, security of selective keyword search can be achieved using the powerful cryptographic technique called encryption. That is, the data that are to be outsourced have to be encrypted before storing it on cloud. Symmetric encryption is used between the data owner and the customer, whereas asymmetric encryption is used between the data owner and the cloud server. In such cases, the data discovery and retrieval have become more stimulating process. However, there are many methodologies developed for searching the encrypted outsourced data from the cloud. In this paper, the efficient data discovery process is focused and enhanced with the developed model called fuzzy-based semantic search for secure data discovery (FSS-SDD). A multi-valued logic called fuzzy logic is used, which has truth values of variables ranging between 0 and 1. It is used where the truth values range between completely true and completely false. The fuzzy logic-based semantic search improves the searching experience of the end user, by finding and retrieving the exact matching files for corresponding search files given by the user. Moreover, the model discovers the closely relevant matches using the semantic similarities, in cases, when the exact matches are not avail. For reducing the false positive rates, grading mechanism is enforced. Using the proposed FSS-SDD model, the processing overhead on new updates is effectively reduced and security in data retrieval is guaranteed.