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Academic literature on the topic 'Ranked Multi-keyword Search and Tree Based Indexing'
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Journal articles on the topic "Ranked Multi-keyword Search and Tree Based Indexing"
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Bhavya, M*, J. Thriveni, and K. R. Venugopal. "EFURMS: An Efficient Scheme for File Upload and Ranked M ulti Keyword Search over Encrypted Data in Cloud." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 4 (2021): 224–31. https://doi.org/10.35940/ijitee.D8464.0210421.
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Cloud based services provide scalable storage capacities and enormous computing capability to enterp r ises and individuals to support big data operations in different sectors like banking, scientific research and health care. Therefore many data owners are interested to outsource their data to cloud storage servers due to their huge advantage in data proc e ssing. However, as the banking and health records usually contain sensitive data, there are privacy concerns if the data gets leaked to un trusted third parties in cloud storage. To protect data from leakage, the widely used technique is to encrypt the da t a before uploading into cloud storage servers. The traditional methods implemented by many authors consumes more time to outsource the data and searching for a document is also time consuming. Sometimes there may be chances of data leakage due to insuffic i ent security. To resolve these issues, in the current VPSearch(VPS) scheme is implemented, which provides features like verifiability of search results and privacy preservation. With its features the current system consumes more time for file uploading an d index generation, which slows down the searching process. In the existing VPS scheme time minimization to efficiently search for a particular document is a challenging task on the cloud. To resolve all the above drawbacks, we have designed an index gene r ation scheme using a tree structure along with a search algorithm using Greedy Depth first technique, that reduces the time for uploading files and file searching time. The newly implemented scheme minimizes the time required to form the index tree file f o r set of files in the document which are to be uploaded and helps in storing the files in a index tree format. These techniques result in reducing the document upload time and speeding up the process of accessing data efficiently using multi keyword searc h with top --'K' value.
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Gaikwad, Suchetadevi M., and Sanjay B. Thakare. "Enhanced Crawler with Multiple Search Techniques using Adaptive Link-Ranking and Pre-Query Processing." Circulation in Computer Science 1, no. 1 (2016): 40–44. http://dx.doi.org/10.22632/ccs-2016-251-24.
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As deep web enlarges; there has been increased interest in methods which help efficiently trace deep-web interfaces. However, because of huge volume and varying nature of deep-web, achieving wide coverage and high efficiency is difficult issue. We proposed a three stage framework, an Enhanced Crawler, for efficiently gathering deep web interfaces. In first stage, enhanced crawler performs site based searching of center pages using automated search engines, avoiding visiting an oversized variety of pages and consuming time. In second stage, enhanced crawler achieves quick in site browsing by fetching most relevant links with associate degree of reconciling link ranking. For further enhancement, our system ranks and priorities websites and also uses a link tree data structure to achieve deep coverage. In third stage, our system provides pre-query processing mechanism so as to help users to write their search query easily by providing char by char keyword search with ranked indexing.
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CHI, PIN-HAO, GRANT SCOTT, and CHI-REN SHYU. "A FAST PROTEIN STRUCTURE RETRIEVAL SYSTEM USING IMAGE-BASED DISTANCE MATRICES AND MULTIDIMENSIONAL INDEX." International Journal of Software Engineering and Knowledge Engineering 15, no. 03 (2005): 527–45. http://dx.doi.org/10.1142/s0218194005002439.
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Indexing protein tertiary structures has been shown to provide a scalable solution for structure-to-structure comparisons in large protein structure retrieval systems. To conduct similarity searches against 53,356 polypeptide chains in a database with real-time responses, two critical issues must be addressed, information extraction and suitable indexing. In this paper, we apply computer vision techniques to extract the predominant information encoded in each 2D distance matrix, generated from 3D coordinates of protein chains. Distance matrices are capable of representing specific protein structural topologies, and similar proteins will generate similar matrices. Once meaningful features are extracted from distance images, an advanced indexing structure, Entropy Balanced Statistical (EBS) k-d tree, can be utilized to index the multidimensional data. With a limited amount of training data from domain experts, namely structural classification of a subset of available protein chains, we apply various techniques in the pattern recognition field to determine clusters of proteins in the multi-dimensional feature space. Our system is able to recall search results in a ranked order from the protein database in seconds, exhibiting a reasonably high degree of precision.
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Renugha, K., P. Shanthi, and A. Umamakeswari. "Multi-Keyword Ranked Search in Cloud Storage using Homomorphic Indexing." International Journal of Engineering & Technology 7, no. 2.24 (2018): 243. http://dx.doi.org/10.14419/ijet.v7i2.24.12057.
Full textAbstract:
In the cloud environment, the main issue is outsourcing of the information to the cloud service provider and outsider. Consider this, the cloud tenant store data in an encrypted form to achieve data security and privacy. The data owner needs the secure information sharing from the cloud and without leak of access pattern to the eavesdroppers. XOR homomorphic encryption searchable algorithm along with ranking is proposed to provide the security over the network. In addition our scheme provides secure Multi-keyword ranked search over encrypted data. Efficient ranked search algorithm returns the relevant document based on the results for the given multiple keywords. The experimental results prove that the system is efficient.
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Dai, Hua, Xuelong Dai, Xiao Li, Xun Yi, Fu Xiao, and Geng Yang. "A Multibranch Search Tree-Based Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data." Security and Communication Networks 2020 (January 23, 2020): 1–15. http://dx.doi.org/10.1155/2020/7307315.
Full textAbstract:
In the interest of privacy concerns, cloud service users choose to encrypt their personal data before outsourcing them to cloud. However, it is difficult to achieve efficient search over encrypted cloud data. Therefore, how to design an efficient and accurate search scheme over large-scale encrypted cloud data is a challenge. In this paper, we integrate bisecting k-means algorithm and multibranch tree structure and propose the α-filtering tree search scheme based on bisecting k-means clusters. The novel index tree is built from bottom-up, and a greedy depth first algorithm is used for filtering the nonrelevant document cluster by calculating the relevance score between the filtering vector and the query vector. The α-filtering tree can improve the efficiency without the loss of search accuracy. The experiment on a real-world dataset demonstrates the effectiveness of our scheme.
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Shanthi, P., and A. Umamakeswari. "Efficient top representative for multi-authorship encrypted cloud data to assist cognitive search." Journal of Intelligent & Fuzzy Systems 39, no. 6 (2020): 8079–89. http://dx.doi.org/10.3233/jifs-189130.
Full textAbstract:
Cloud computing is gaining ground in the digital and business world. It delivers storage service for user access using Internet as a medium. Besides the numerous benefits of cloud services, migrating to public cloud storage leads to security and privacy concerns. Encryption method protects data privacy and confidentiality. However, encrypted data stored in cloud storage reduces the flexibility in processing data. Therefore, the development of new technologies to search top representatives from encrypted public storage is the current requirement. This paper presents a similarity-based keyword search for multi-author encrypted documents. The proposed Authorship Attribute-Based Ranked Keyword Search (AARKS) encrypts documents using user attributes, and returns ranked results to authorized users. The scheme assigns weight to index vectors by finding the dominant keywords of the specific authority document collection. Search using the proposed indexing prunes away branches and processes only fewer nodes. Re-weighting documents using the relevant feedback also improves user experience. The proposed scheme ensures the privacy and confidentiality of data supporting the cognitive search for encrypted cloud data. Experiments are performed using the Enron dataset and simulated using a set of queries. The precision obtained for the proposed ranked retrieval is 0.7262. Furthermore, information leakage to a cloud server is prevented, thereby proving its suitability for public storage.
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Dr.K.V.Ranga, Rao*1 &. Dr.B.Vijayakumar2. "MULTIKEYWORD SEARCH SCHEMA FOR CLOUD DATA." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 5, no. 4 (2018): 21–30. https://doi.org/10.5281/zenodo.1216749.
Full textAbstract:
Due to the increasing popularity of cloud computing, more and more data owners are motivated to outsource their data to cloud servers for great convenience and reduced cost in data management. However, sensitive data should be encrypted before outsourcing for privacy requirements, which obsoletes data utilization like keyword-based document retrieval. In this paper, we present a secure multi-keyword ranked search scheme over encrypted cloud data, which simultaneously supports dynamic update operations like deletion and insertion of documents. Specifically, the vector space model and the widely-used TF IDF model are combined in the index construction and query generation. We construct a special tree-based index structure and propose a Greedy Depth-first Search algorithm to provide efficient multi-keyword ranked search. The secure KNN algorithm is utilized to encrypt the index and query vectors, and meanwhile ensure accurate relevance score calculation between encrypted index and query vectors. In order to resist statistical attacks, phantom terms are added to the index vector for blinding search results. Due to the use of our special tree-based index structure, the proposed scheme can achieve sub-linear search time and deal with the deletion and insertion of documents flexibly. Extensive experiments are conducted to demonstrate the efficiency of the proposed scheme
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Hu, Zheng, Hua Dai, Geng Yang, Xun Yi, and Wenjie Sheng. "Semantic-Based Multi-Keyword Ranked Search Schemes over Encrypted Cloud Data." Security and Communication Networks 2022 (April 29, 2022): 1–15. http://dx.doi.org/10.1155/2022/4478618.
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Traditional searchable encryption schemes construct document vectors based on the term frequency-inverse document frequency (TF-IDF) model. Such vectors are not only high-dimensional and sparse but also ignore the semantic information of the documents. The Sentence Bidirectional Encoder Representations from Transformers (SBERT) model can be used to train vectors containing document semantic information to realize semantic-aware multi-keyword search. In this paper, we propose a privacy-preserving searchable encryption scheme based on the SBERT model. The SBERT model is used to train vectors containing the semantic information of documents, and these document vectors are then used as input to the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) clustering algorithm. The HDBSCAN algorithm generates a soft cluster membership vector for each document. We treat each cluster as a topic, and the vector represents the probability that the document belongs to each topic. According to the clustering process in the schemes, the topic-term frequency-inverse topic frequency (TTF-ITF) model is proposed to generate keyword topic vectors. Through the SBERT model, searchable encryption scheme can achieve more precise semantic-aware keyword search. At the same time, the special index tree is used to improve search efficiency. The experimental results on real datasets prove the effectiveness of our scheme.
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Kuldeep P. Sambrekar, Narendra Shyam Joshi ,. "Privacy-Preserving and Ranked Search Using Advanced Multi-Keyword Scheme Over the Encrypted Cloud Environment." Journal of Electrical Systems 20, no. 1s (2024): 353–65. http://dx.doi.org/10.52783/jes.776.
Full textAbstract:
In order to enable users to extract useful insights on demand, this paper presents a unique approach for secure and privacy-preserving ranked search in cloud environments. There is a rising need for strong systems that strike a balance between data utility and individual privacy as cloud-based data processing and storage become more and more prevalent. Our suggested methodology makes use of sophisticated multi-keyword operators and encryption methods to offer a safe and effective solution for cloud-based ranked search that protects privacy. Our methodology's essential component is the combination of a safe encryption technique and sophisticated multi-keyword search operators. With this combination, users can do ranked searches on encrypted data while maintaining the privacy of the underlying data. Our approach leverages strong cryptography protocols and an advanced indexing mechanism to provide rapid retrieval of relevant results while protecting confidential information from unauthorized individuals. Secure query processing techniques and encryption are added for added privacy protection. This guarantees that throughout the ranking search operations, the decrypted data cannot be accessed by the cloud service provider or any other parties participating in the data processing pipeline. Users don't have to compromise the privacy of their stored data or expose the exact text of their queries to obtain ranked results based on several keywords. Our technology is especially useful in situations when customers need instantaneous insights from data stored in the cloud without compromising privacy. Our technology protects the secrecy of the underlying datasets while enabling customers to obtain secure insights on demand in a variety of sensitive sectors, including financial analysis and medical research. The suggested method offers a strong and adaptable way to achieve privacy-preserving ranked search in cloud environments. Through the incorporation of sophisticated multi-keyword operators and encryption strategies, our approach guarantees users may effectively obtain pertinent data while maintaining personal privacy. With its ability to provide safe, anytime access to insights stored in cloud-based data repositories, this breakthrough has enormous potential for a variety of applications.
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Lin, Je-Kuan, Wun-Ting Lin, and Ja-Ling Wu. "Flexible and Efficient Multi-Keyword Ranked Searchable Attribute-Based Encryption Schemes." Cryptography 7, no. 2 (2023): 28. http://dx.doi.org/10.3390/cryptography7020028.
Full textAbstract:
Currently, cloud computing has become increasingly popular and thus, many people and institutions choose to put their data into the cloud instead of local environments. Given the massive amount of data and the fidelity of cloud servers, adequate security protection and efficient retrieval mechanisms for stored data have become critical problems. Attribute-based encryption brings the ability of fine-grained access control and can achieve a direct encrypted data search while being combined with searchable encryption algorithms. However, most existing schemes only support single-keyword or provide no ranking searching results, which could be inflexible and inefficient in satisfying the real world’s actual needs. We propose a flexible multi-keyword ranked searchable attribute-based scheme using search trees to overcome the above-mentioned problems, allowing users to combine their fuzzy searching keywords with AND–OR logic gates. Moreover, our enhanced scheme not only improves its privacy protection but also goes a step further to apply a semantic search to boost the flexibility and the searching experience of users. With the proposed index-table method and the tree-based searching algorithm, we proved the efficiency and security of our schemes through a series of analyses and experiments.