An Efficient Privacy-Preserving Ranked Keyword Search Method

ABSTRACT:

Cloud data owners prefer to outsource documents in an encrypted form for the purpose of privacy preserving. Therefore it is essential to develop efficient and reliable ciphertext search techniques. One challenge is that the relationship between documents will be normally concealed in the process of encryption, which will lead to significant search accuracy performance degradation. Also the volume of data in data centers has experienced a dramatic growth. This will make it even more challenging to design ciphertext search schemes that can provide efficient and reliable online information retrieval on large volume of encrypted data. In this paper, a hierarchical clustering method is proposed to support more search semantics and also to meet the demand for fast ciphertext search within a big data environment. The proposed hierarchical approach clusters the documents based on the minimum relevance threshold, and then partitions the resulting clusters into sub-clusters until the constraint on the maximum size of cluster is reached. In the search phase, this approach can reach a linear computational complexity against an exponential size increase of document collection. In order to verify the authenticity of search results, a structure called minimum hash sub-tree is designed in this paper. Experiments have been conducted using the collection set built from the IEEE Xplore. The results show that with a sharp increase of documents in the dataset the search time of the proposed method increases linearly whereas the search time of the traditional method increases exponentially. Furthermore, the proposed method has an advantage over the traditional method in the rank privacy and relevance of retrieved documents.

EXISTING SYSTEM:

In the recent years, researchers have proposed many ciphertext search schemes by incorporating the cryptography techniques. In addition, the relationship between documents is concealed in the above methods. The relationship between documents represents the properties of the documents and hence maintaining the relationship is vital to fully express a document. For example, the relationship can be used to express its category. If a document is independent of any other documents except those documents that are related to sports, then it is easy for us to assert this document belongs to the category of the sports.
Due to the blind encryption, this important property has been concealed in the traditional methods. Therefore, proposing a method which can maintain and utilize this relationship to speed the search phase is desirable.
Sun et al. use Merkle hash tree and cryptographic signature to create a verifiable MDB-tree. However, their work cannot be directly used in our architecture which is oriented for privacy-preserving multiple keyword search. Thus, a proper mechanism that can be used to verify the search results within big data scenario is essential to both the CSPs and end users.

DISADVANTAGES OF EXISTING SYSTEM:

Existing methods have been proven with provable security, but their methods need massive operations and have high time complexity. Therefore, former methods are not suitable for the big data scenario where data volume is very big and applications require online data processing.
Song et al. method has a high searching cost due to the scanning of the whole data collection word by word.
Due to the lack of rank mechanism, users have to take a long time to select what they want when massive documents contain the query keyword. Thus, the order-preserving techniques are utilized to realize the rank mechanism,
Sun et al. give a new architecture which achieves better search efficiency. However, at the stage of index building process, the relevance between documents is ignored.