ABSTRACT:
With the increasing adoption of cloud
computing, a growing number of users outsource their datasets to cloud. To
preserve the privacy, the datasets are usually encrypted before outsourcing.
However, the common practice of encryption makes the effective utilization of
the data difficult. For example, it is difficult to search the given keywords
in encrypted datasets. Many schemes are proposed to make encrypted data
searchable based on keywords. However, keyword-based search schemes ignore the
semantic representation information of users retrieval, and cannot completely
meet with users search intention. Therefore, how to design a content-based
search scheme and make semantic search more effective and context-aware is a
difficult challenge. In this paper, we propose ECSED, a novel semantic search
scheme based on the concept hierarchy and the semantic relationship between
concepts in the encrypted datasets. ECSED uses two cloud servers. One is used
to store the outsourced datasets and return the ranked results to data users.
The other one is used to compute the similarity scores between the documents
and the query and send the scores to the first server. To further improve the
search efficiency, we utilize a tree-based index structure to organize all the
document index vectors. We employ the multi-keyword ranked search over
encrypted cloud data as our basic frame to propose two secure schemes. The
experiment results based on the real world datasets show that the scheme is
more efficient than previous schemes. We also prove that our schemes are secure
under the known ciphertext model and the known background model.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
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System : Pentium Dual Core.
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Hard Disk : 120 GB.
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Monitor : 15’’ LED
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Input Devices : Keyboard, Mouse
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Ram : 1 GB
SOFTWARE REQUIREMENTS:
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Operating system : Windows 7.
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Coding Language : JAVA/J2EE
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Tool : Netbeans 7.2.1
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Database : MYSQL
REFERENCE:
Zhangjie Fu Lili Xia Xingming Sun
Alex X. Liu Guowu Xie, “Semantic-aware Searching over Encrypted Data for Cloud Computing”,
IEEE Transactions on Information Forensics and Security, 2018.