Privacy-Enabled Multi-Keyword Search Over Encrypted Cloud Data

Abstract

As cloud data storage gets big and big, many users who own data that are too big in size are interested to move their data to a remote cloud. For the purpose of keeping users’ sensitive data secure, these sensitive users’ data must be encrypted in an offline stage. There exist many searchable encryption algorithms to prove data existence. In whatever way, the existing search algorithms do give too little or no consideration whether data users’ queries were efficient or not in a system that supports many data owners. The methods developed in this work is a multiple keyword searches that supports search results in a ranked manner which is based on a tree structure. As the data that users are interested in outsourcing to the remote cloud may be of a big size, term frequency and inverted document frequency which is the most appropriate method for ranking results of users’ queries according to certain similarity criteria are used. To make the cloud server accomplish a secure data retrieval without knowing any users’ private data, a novel search algorithm that retains privacy and also which is lay on bi-linear mapping is developed. To attain a timely search, for every data owner, an index that is developed in a tree structure with additive order and privacy retaining method family is established. After then by using a Merkle tree algorithm to look for the files of interest cloud server combine these indexes effectively. Lastly, a test that is done on the security of the proposed system makes sure that the developed work is secure, and the performance test makes it clear that it is efficient.