Enabling Central Keyword-Based Semantic Extension Search Over Encrypted Outsourced Data

Fu, Zhangjie; Wu, Xinle; Wang, Qian; Ren, Kui

doi:10.1109/tifs.2017.2730365

Cited by 75 publications

(29 citation statements)

References 29 publications

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“…13 The tree's decision-making process needs to start from the root. This is a classification method based on tree structure.…”

Section: The Common Methods Of Text Classificationmentioning

confidence: 99%

“…This is a classification method based on tree structure. 13 The tree's decision-making process needs to start from the root. The data to be measured are compared with the feature node in the decision tree, and the next branch is selected according to the comparison result, until the leaf node gets the final decision result.…”

Section: The Common Methods Of Text Classificationmentioning

confidence: 99%

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Research on improved text classification method based on combined weighted model

Wang

Zhu

2019

Concurrency and Computation

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Text classification is very important in information retrieval, but the traditional text classification model has many problems, such as the feature dimension disaster, the lack of semantic features, etc. Aiming at the problems, this paper proposes an improved TFIDF model combined with the Word2vec model for weighing word vectors. In view of the inability of the Word2vec model to distinguish the importance of words with the text, TFIDF is further introduced to weighing Word2vec word vectors to achieve a weighted Word2vec classification model. For data preprocessing, we optimized the traditional StringToWordVector algorithm. The main improvement of StringToWordVector is the introduction to a new algorithm of stem extraction.First, this paper gives a simple description of the basic steps and algorithms of traditional text classification, and then, the ideas and steps of the improved StringToWordVector algorithm are proposed. Finally, experimental results using our improved algorithm are tested for four different data sets (WEBO_SINA and three standard UCI data sets). The experimental results show that the improved StringToWordVector algorithm combined with the combined weighted model has higher classification accuracy, recall, and F1 values than the traditional text classification model only using the Word2vec model or using TFIDF. The experimental results are satisfactory.Short text classification of a microblog has a great research value of mining users' interest, hot topic discovery, and a personalized recommendation system. Text categorization is a supervised learning process that defines a category for each document in document sets according to a predefined subject. With the arrival of the big data era, the number of electronic documents on the Internet has increased significantly. Text classification has become the key technology of information retrieval and management. Text classification needs to convert text into data format that a computer can process; traditional document representation methods use the information retrieval technology, such as continuous bag-of-words (shortly as CBOW) and term frequency-inverse document frequency (shortly as TFIDF). In the CBOW model, documents are regarded as disordered vocabulary sets, ignoring its grammar and word order. TFIDF is a popular weighting technique in the information retrieval field for measuring the importance of a word with a document or corpus. The vector space model is a popular model for document representation; it achieves better results from dealing with long text classification. However, compared with the long text, the short text has the characteristics of sparsity, real time, and nonstandard. The sparsity of VSM will reduce the accuracy of the short text classification. For the sparse problem of the vector space model, one way is to extend the short text with the aid of an external knowledge base (such as Wikipedia, WordNet, and HowNet). Lin et al used the upper and lower relations of the words of the net to extend the short text 1 ; Joachims ext...

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“…13 The tree's decision-making process needs to start from the root. This is a classification method based on tree structure.…”

Section: The Common Methods Of Text Classificationmentioning

confidence: 99%

Section: The Common Methods Of Text Classificationmentioning

confidence: 99%

Research on improved text classification method based on combined weighted model

Wang

Zhu

2019

Concurrency and Computation

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“…30,45 These weights are typically hidden using deterministic encryption methods. The weight of an extracted keyword can be obtained based on its importance in the document.…”

Section: One-to-many Order Preserving Symmetric Encryption (Om-opse)mentioning

confidence: 99%

“…The weight of an extracted keyword can be obtained based on its importance in the document. 30,45 These weights are typically hidden using deterministic encryption methods. 46 However, because deterministic encryption is a one-to-one mapping of plain text to cipher text, this encryption method can potentially leak some information about the documents.…”

Section: One-to-many Order Preserving Symmetric Encryption (Om-opse)mentioning

confidence: 99%

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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“…However, other evaluation criteria are not discussed in depth, such as the well-known consistency criterion (e.g., the inferred friend consistency) suggested in work [25]. Besides, as [26] indicates, weight plays an important role in the final evaluation results; however, we do not consider the weight of found friends in this paper for simplicity.…”

Section: Shortcoming Analysesmentioning

confidence: 99%

Privacy-Preserving and Scalable Service Recommendation Based on SimHash in a Distributed Cloud Environment

Dou

et al. 2017

Complexity

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With the increasing volume of web services in the cloud environment, Collaborative Filtering-(CF-) based service recommendation has become one of the most effective techniques to alleviate the heavy burden on the service selection decisions of a target user. However, the service recommendation bases, that is, historical service usage data, are often distributed in different cloud platforms. Two challenges are present in such a cross-cloud service recommendation scenario. First, a cloud platform is often not willing to share its data to other cloud platforms due to privacy concerns, which decreases the feasibility of cross-cloud service recommendation severely. Second, the historical service usage data recorded in each cloud platform may update over time, which reduces the recommendation scalability significantly. In view of these two challenges, a novel privacy-preserving and scalable service recommendation approach based on SimHash, named SerRec SimHash , is proposed in this paper. Finally, through a set of experiments deployed on a real distributed service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation.

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Enabling Central Keyword-Based Semantic Extension Search Over Encrypted Outsourced Data

Cited by 75 publications

References 29 publications

Research on improved text classification method based on combined weighted model

Research on improved text classification method based on combined weighted model

Survey on secure search over encrypted data on the cloud

Privacy-Preserving and Scalable Service Recommendation Based on SimHash in a Distributed Cloud Environment

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