Enabling Fine-grained Multi-keyword Search Supporting Classified Sub-dictionaries over Encrypted Cloud Data
Using cloud computing, individuals can store their data on remote servers and allow data access to public users through the cloud servers. As the outsourced data are likely to contain sensitive privacy information, they are typically encrypted before uploaded to the cloud. This, however, significantly limits the usability of outsourced data due to the difficulty of searching over the encrypted data. In this paper, we address this issue by developing the fine-grained multi-keyword search schemes over encrypted cloud data. Our original contributions are three-fold. First, we introduce the relevance scores and preference factors upon keywords which enable the precise keyword search and personalized user experience. Second, we develop a practical and very efficient multi-keyword search scheme. The proposed scheme can support complicated logic search the mixed “AND”, “OR” and “NO” operations of keywords. Third, we further employ the classified sub-dictionaries technique to achieve better efficiency on index building, trapdoor generating and query. Lastly, we analyze the security of the proposed schemes in terms of confidentiality of documents, privacy protection of index and trapdoor, and unlinkability of trapdoor. Through extensive experiments using the real-world dataset, we validate the performance of the proposed schemes. Both the security analysis and experimental results demonstrate that the proposed schemes can achieve the same security level comparing to the existing ones and better performance in terms of functionality, query complexity and efficiency.
PROJECT OUTPUT VIDEO:
The searchable encryption has been recently developed as a fundamental approach to enable searching over encrypted cloud data, which proceeds the following operations.
Wang et al. propose a ranked keyword search scheme which considers the relevance scores of keywords.
Sun et al. propose a multi-keyword text search scheme which considers the relevance scores of keywords and utilizes a multidimensional tree technique to achieve efficient search query.
Yu et al. propose a multi-keyword top-k retrieval scheme which uses fully homomorphic encryption to encrypt the index/trapdoor and guarantees high security.
Cao et al. propose a multi-keyword ranked search (MRSE), which applies coordinate machine as the keyword matching rule, i.e., return data with the most matching keywords.
DISADVANTAGES OF EXISTING SYSTEM:
Due to using order-preserving encryption (OPE) to achieve the ranking property, the existing scheme cannot achieve unlinkability of trapdoor.
Although many search functionalities have been developed in previous literature towards precise and efficient searchable encryption, it is still difficult for searchable encryption to achieve the same user experience as that of the plaintext search, like Google search.
Most existing proposals can only enable search with single logic operation, rather than the mixture of multiple logic operations on keywords
In this work, we address by developing two Fine-grained Multi-keyword Search (FMS) schemes over encrypted cloud data.
In this system, we introduce the relevance scores and the preference factors of keywords for searchable encryption. The relevance scores of keywords can enable more precise returned results, and the preference factors of keywords represent the importance of keywords in the search keyword set specified by search users and correspondingly enables personalized search to cater to specific user preferences. It thus further improves the search functionalities and user experience.
In this system, we realize the “AND”, “OR” and “NO” operations in the multi-keyword search for searchable encryption. Compared with schemes, the proposed scheme can achieve more comprehensive functionality and lower query complexity.
In this system, we employ the classified sub-dictionaries technique to enhance the efficiency of the above two schemes. Extensive experiments demonstrate that the enhanced schemes can achieve better efficiency in terms of index building, trapdoor generating and query in the comparison with schemes
ADVANTAGES OF PROPOSED SYSTEM:
Better search results with multi-keyword query by the cloud server according to some ranking criteria.
To reduce the communication cost.
Achieves lower query complexity.
Achieves better efficiency in index building scheme of our proposed model.
The data owner outsources her data to the cloud for convenient and reliable data access to the corresponding search users. To protect the data privacy, the data owner encrypts the original data through symmetric encryption. To improve the search efficiency, the data owner generates some keywords for each outsourced document. The corresponding index is then created according to the keywords and a secret key. After that, the data owner sends the encrypted documents and the corresponding indexes to the cloud, and sends the symmetric key and secret key to search users.
The cloud server is an intermediate entity which stores the encrypted documents and corresponding indexes that are received from the data owner, and provides data access and search services to search users. When a search user sends a keyword trapdoor to the cloud server, it would return a collection of matching documents based on certain operations.
A search user queries the outsourced documents from the cloud server with following three steps. First, the search user receives both the secret key and symmetric key from the data owner. Second, according to the search keywords, the search user uses the secret key to generate trapdoor and sends it to the cloud server. Last, she receives the matching document collection from the cloud server and decrypts them with the symmetric key.
System : Pentium IV 2.4 GHz.
Hard Disk : 40 GB.
Floppy Drive : 44 Mb.
Monitor : 15 VGA Colour.
Ram : 512 Mb.
Operating system : Windows XP/7.
Coding Language : net, C#.net
Tool : Visual Studio 2010
Database : SQL SERVER 2008
Hongwei Li, Member, IEEE, Yi Yang, Student Member, IEEE, Tom H. Luan, Member, IEEE, Xiaohui Liang, Student Member, IEEE, Liang Zhou, Member, IEEE, and Xuemin (Sherman) Shen, Fellow, IEEE, “Enabling Fine-grained Multi-keyword SearchSupporting Classified Sub-dictionaries over Encrypted Cloud Data”, IEEE Transactions on Dependable and Secure Computing 2016