A Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data

ABSTRACT

Due to the increasing popularity of cloud computing, more and more data owners are motivated to outsource their data to cloud servers for great convenience and reduced cost in data management. However, sensitive data should be encrypted before outsourcing for privacy requirements, which obsoletes data utilization like keyword-based document retrieval. In this paper, we present a secure multi-keyword ranked search scheme over encrypted cloud data, which simultaneously supports dynamic update operations like deletion and insertion of documents. Specifically, the vector space model and the widely-used TFIDF model are combined in the index construction and query generation. We construct a special tree-based index structure and propose a “Greedy Depth-first Search” algorithm to provide efficient multi-keyword ranked search. The secure kNN algorithm is utilized to encrypt the index and query vectors, and meanwhile ensure accurate relevance score calculation between encrypted index and query vectors. In order to resist statistical attacks, phantom terms are added to the index vector for blinding search results. Due to the use of our special tree-based index structure, the proposed scheme can achieve sub-linear search time and deal with the deletion and insertion of documents flexibly. Extensive experiments are conducted to demonstrate the efficiency of the proposed scheme.

AIM

The main aim of this paper is present a secure multi-keyword ranked search scheme over encrypted cloud data, which simultaneously supports dynamic update operations like deletion and insertion of documents.

SCOPE

The scope of this paper is due to the use of our special tree-based index structure the proposed scheme can achieve sub-linear search time and deal with the deletion and insertion of documents flexibly

EXISTING SYSTEM

Presented a secure multi-keyword search scheme that supports similarity-based ranking. The authors constructed a searchable index tree based on vector space model and adopted cosine measure together with TF×IDF to provide ranking results. search algorithm achieves better-than-linear search efficiency but results in precision loss. proposed a secure multi-keyword search method which utilized local sensitive hash (LSH) functions to cluster the similar documents. The LSH algorithm is suitable for similar search but cannot provide exact ranking. In  proposed a scheme to deal with secure multi-keyword ranked search in a multi-owner model. In this scheme, different data owners use different secret keys to encrypt their documents and keywords while authorized data users can query without knowing keys of these different data owners. The authors proposed an “Additive Order Preserving Function” to retrieve the most relevant search results. However, these works don’t support dynamic operations.

DISADVANTAGES

1.     Existing System does not supports dynamic update operations like deletion and insertion of documents
2.     Downloading all the data from the cloud and decrypt locally is obviously impractical.

 PROPOSED SYSTEM

In this paper proposes a secure tree-based search scheme over the encrypted cloud data, which supports multi keyword ranked search and dynamic operation on the document collection. Specifically, the vector space model and the widely-used “term frequency (TF)  inverse document frequency (IDF)” model are combined in the index construction and query generation to provide multi keyword ranked search. In order to obtain high search efficiency, we construct a tree-based index structure and propose a “Greedy Depth-first Search” algorithm based on this index tree. Due to the special structure of our tree-based index, the proposed search scheme can flexibly achieve sub-linear search time and deal with the deletion and insertion of documents. The secure kNN algorithm is utilized to encrypt the index and query vectors, and meanwhile ensure accurate relevance score calculation between encrypted index and query vectors

 ADVANTAGES

·      A Greedy Depth-first Search algorithm to obtain better efficiency than linear search

·      It supports dynamic update operations like deletion and insertion of documents

·      The parallel search process can be carried out to further reduce the time cost.

System Architecture

System Configuration

Hardware Requirements

• Speed                        -   1.1 Ghz
• Processor              - Pentium IV
• RAM                         -   512 MB (min)
• Hard Disk         -    40 GB
• Key Board                 -     Standard Windows Keyboard
• Mouse                       -    Two or Three Button Mouse
• Monitor                     -     LCD/LED

 Software requirements

• Operating System              : Windows 7             
•  Front End                           : ASP.Net and C#
• Database                             : MSSQL
• Tool                                    : Microsoft Visual studio

References

Wang, X.  Sun, X. Wang, Q. Xia, Z. “A Secure and Dynamic Multi-keyword Ranked Search Scheme over Encrypted Cloud Data” IEEE Transactions on Parallel and Distributed Systems Volume PP, Issue 99 February 2015.