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

Xia, Zhihua; Zhu, Yanling; Sun, Xingming; Chen, Lihong

doi:10.1186/s13677-014-0008-2

Cited by 53 publications

(38 citation statements)

References 19 publications

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

Section: Overviewmentioning

confidence: 99%

“…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.''…”

Section: Setup Mechanismmentioning

confidence: 99%

“…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.…”

Section: Retrieval Mechanismmentioning

confidence: 99%

“…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.…”

Section: Ontological Semantic Searchmentioning

confidence: 99%

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Survey on secure search over encrypted data on the cloud

Pham

Woodworth

Salehi

2019

Concurrency and Computation

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Cloud computing has become a potential resource for businesses and individuals to outsource their data to remote but highly accessible servers. However, the potential of cloud services has not been fully realized due to users concerns about data privacy and security. User-side encryption techniques can be employed to mitigate the security concerns, but once the data is encrypted, no processing (eg, searching) can be performed on the outsourced data. Searchable Encryption (SE) techniques have been widely studied to enable searching on the data while they are encrypted. These techniques enable various types of search on the encrypted data and offer different levels of security. While these techniques enable different search types and vary in details, they share similarities in their components and architectures. In this paper, we provide a comprehensive survey on different secure search techniques, a high-level architecture for these systems, and an analysis of their performance and security level. KEYWORDScloud security, encrypted search, search over encrypted data, survey INTRODUCTIONAs cloud computing becomes prevalent, more cloud-based solutions are being developed and widely used in different applications. Companies that have adopted cloud storage solutions are reported to gain a competitive edge against those that have not. 1Cloud computing is favored due to its many advantages, including convenience and accessibility, consistent back ups to reducing the burden of local storage, and saving capital expenditure on in-house hardware and software maintenance. 2 However, public cloud storage services may be utilized by multi-tenant customers who store large amounts of potentially sensitive data on the cloud. Using cloud storage implies losing full control over data and delegating it to the cloud administrators, exposing the data to potential external and internal attacks, 3,4 which can be devastating for organizations that rely on confidentiality of their data (eg, financial corporations).These problems have made businesses concerned about outsourcing their data to the cloud and utilizing its potential. 5,6 For instance, a medical center that owns patients' health records cannot outsource its data to a cloud that is vulnerable to attacks, due to legal regulations. 7 In another instance, a law enforcement agency that keeps sensitive criminal records will hesitate to use cloud storage because of similar concerns.this approach remains impractical. Therefore, searchable encryption systems (eg, see other works 12-15 ) have been introduced to cope with this problem. These systems ideally allow the encrypted data to be searched without revealing the data and search query. Hence, they relieve concerns about data confidentiality in the cloud. Efforts to create searchable encryption systems date back to 2000 with work by Song et al. 11 Since then, numerous research works have been undertaken to enable different types of searchable encryption. Although these systems differ in their search approaches, security level, and perf...

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

Section: Overviewmentioning

confidence: 99%

“…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.''…”

Section: Setup Mechanismmentioning

confidence: 99%

Section: Retrieval Mechanismmentioning

confidence: 99%

Section: Ontological Semantic Searchmentioning

confidence: 99%

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Survey on secure search over encrypted data on the cloud

Pham

Woodworth

Salehi

2019

Concurrency and Computation

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show abstract

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Singh¹

2017

IJCIS

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Effective query expansion terms selection methods are really very important for improving the accuracy and efficiency of Pseudo-Relevance Feedback (PRF) based automatic query expansion techniques in information retrieval system. These methods remove irrelevant and redundant terms from the top retrieved feedback documents with respect to a user query. Individual terms selection methods have been widely investigated for improving its performance. However, it is always a challenging task to find an individual expansion terms selection method that would outperform other individual methods in most cases. In this paper, first we explore the possibility of improving the overall performance using individual terms selection methods. Second, we propose a model for combining multiple expansion terms selection methods by using a variety of ranks combining approaches. Third, semantic filtering used to filter out semantically irrelevant terms obtained after combining multiple terms selection methods. Fourth, the Genetic Algorithm used to make an optimal combination of query terms and candidate expansion terms obtained by applying ranks combination and semantic filtering approach. Our experimental results demonstrated that our proposed approaches achieved a significant improvement over each individual terms selection methods and related state-of-the-arts approaches.

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Accuracy‐first and efficiency‐first privacy‐preserving semantic‐aware ranked searches in the cloud

Zhou

Dai

et al. 2022

Int J of Intelligent Sys

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Traditional term frequency-inverse document frequency model-based privacy-preserving ranked search schemes rarely consider the latent semantic meanings of documents and keywords. It is a challenge to design efficient semantic-aware ranked search (SRSE) schemes with privacy preservation. In this paper, two privacypreserving SRSE schemes are developed for the cloud environments. The first scheme is the accuracy-first search scheme. In this scheme, the Latent Dirichlet Allocation topic model is adopted to generate the topicbased semantic information-embedded vectors for documents and queried keywords, which supports semanticaware relevance measurement. The bisecting k-means clustering algorithm is used to build an accuracy-first filtering tree index (AFF-tree), and the AFF-tree-based search algorithm is proposed to achieve the accuracy-first ranked search. The second scheme is the efficiency-first search scheme. It performs a structure optimization on the AFF-tree, and a newly efficiency-first filtering tree index (EFF-tree) is designed. By using the EFF-tree, an anchor node-based search algorithm is designed to achieve the efficiency-first ranked search at the expense of a little decrease in search result precision. The secure inner product is used to perform privacy-preserving semantic-aware relevance measurement between

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

Cited by 53 publications

References 19 publications

Survey on secure search over encrypted data on the cloud

Survey on secure search over encrypted data on the cloud

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

Accuracy‐first and efficiency‐first privacy‐preserving semantic‐aware ranked searches in the cloud

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