Secure semantic expansion based search over encrypted cloud data supporting similarity ranking

Abstract

With the advent of cloud computing, more and more information data are outsourced to the public cloud for economic savings and ease of access. However, the privacy information has to be encrypted to guarantee the security. To implement efficient data utilization, search over encrypted cloud data has been a great challenge. The existing solutions depended entirely on the submitted query keyword and didn't consider the semantics of keyword. Thus the search schemes are not intelligent and also omit some semantically related documents. In view of the deficiency, as an attempt, we propose a semantic expansion based similar search solution over encrypted cloud data. Our solution could return not only the exactly matched files, but also the files including the terms semantically related to the query keyword. In the proposed scheme, a corresponding file metadata is constructed for each file. Then both the encrypted metadata set and file collection are uploaded to the cloud server. With the metadata set, the cloud server builds the inverted index and constructs semantic relationship library (SRL) for the keywords set. After receiving a query request, the cloud server first finds out the keywords that are semantically related to the query keyword according to SRL. Then both the query keyword and the extensional words are used to retrieve the files. The result files are returned in order according to the total relevance score. Eventually, detailed security analysis shows that our solution is privacy-preserving and secure under the previous searchable symmetric encryption (SSE) security definition. Experimental evaluation demonstrates the efficiency and effectives of the scheme.

Survey on secure search over encrypted data on the cloud

“…13,21,27 Then, the encrypted Trapdoor is sent to the cloud server. This process is used by data users to form search queries.…”

“…14,15,21,35 The Index structure is also known as ''meta data'' 18 or ''identify keywords.'' 14,15,21,35 The Index structure is also known as ''meta data'' 18 or ''identify keywords.''…”

“…21,36 In the next section, we provide further details on security aspects of cloud-based searchable encryption systems. 21,36 In the next section, we provide further details on security aspects of cloud-based searchable encryption systems.…”

“…Sun et al 14 20,51 Other works 11,13,25,32,36,37 Semantic Search Other works 14,18,28,35,54 Other works 14,18,28,35 Woodworth et al 28 Other works 26,28,54 Other works 14,15,21,28 of the two schemes called WBSKS. In the SBKS scheme, alongside the process of extracting important keywords of the document, synonyms that represent the semantic of a document are collected.…”

Ranks Aggregation and Semantic Genetic Approach based Hybrid Model for Query Expansion

“…Term association based terms selection methods, such as Mutual Information [3] and Co-occurrence Information [2,4] estimate the goodness of each term based on the occurrence of terms in feedback documents (term pool). Corpus statistics based query expansion term selection methods, such as Chi-Square Statistic [5,6], Binary Independence Model [7] and Robertson Selection Value [8] estimate the goodness of each term based on the distribution of terms across the corpus and using the query term information present in feedback documents.…”