MUSE: An Efficient and Accurate Verifiable Privacy-Preserving Multikeyword Text Search over Encrypted Cloud Data

Zhu, Xiangyang; Dai, Hua; Yi, Xun; Yang, Geng; Xiao, Li

doi:10.1155/2017/1923476

Cited by 15 publications

(5 citation statements)

References 32 publications

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“…Xia et al [11] proposed a secure and dynamic multi-keyword ranked search scheme by adopting a balanced binary tree index. Chen et al [13] and Zhu et al [12] proposed two different privacy-preserving ranked search schemes, which both utilize clustering algorithm to improve search efficiency.…”

Section: Related Workmentioning

confidence: 99%

“…In this model, Pub-Cloud is assumed to have more knowledge than the known ciphertext threat model, such as the keyword frequency statistics of document collection. The statistical information reveals the quantity of documents of specific keywords in D, which could be used by Pub-Cloud to apply TF statistical attacks and hence infer or even recognize certain keywords through analyzing the histogram or value range of the corresponding frequency distributions [11], [12], [29].…”

Section: Problem Descriptionmentioning

confidence: 99%

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A Privacy-Preserving Multi-Keyword Ranked Search Over Encrypted Data in Hybrid Clouds

Dai

Yang

et al. 2020

IEEE Access

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With the rapid development of cloud computing services, more and more individuals and enterprises prefer to outsource their data or computing to clouds. In order to preserve data privacy, the data should be encrypted before outsourcing and it is a challenge to perform searches over encrypted data. In this paper, we propose a privacy-preserving multi-keyword ranked search scheme over encrypted data in hybrid clouds, which is denoted as MRSE-HC. The keyword dictionary of documents is clustered into balanced partitions by a bisecting k-means clustering based keyword partition algorithm. According to the partitions, the keyword partition based bit vectors are adopted for documents and queries which are utilized as the index of searches. The private cloud filters out the candidate documents by the keyword partition based bit vectors, and then the public cloud uses the trapdoor to determine the result in the candidates. On the basis of the MRSE-HC scheme, an enhancement scheme EMRSE-HC is proposed, which adds complete binary pruning tree to further improve search efficiency. The security analysis and performance evaluation show that MRSE-HC and EMRSE-HC are privacy-preserving multi-keyword ranked search schemes for hybrid clouds and outperforms the existing scheme FMRS in terms of search efficiency. INDEX TERMS Hybrid cloud, multi-keyword ranked search, privacy-preserving, searchable encryption.

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Section: Related Workmentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

A Privacy-Preserving Multi-Keyword Ranked Search Over Encrypted Data in Hybrid Clouds

Dai

Yang

et al. 2020

IEEE Access

Self Cite

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“…Moreover, the data owner can share their data with a large number of users which requires the cloud server to have the ability to meet a large amount of requests with effective data retrieval services. One effective method for solving this problem is ranking the results and sending back the top-K files to the data user, rather than all of the relevant files [14]. This method can dramatically reduce the communication overhead and still meet user's demand.…”

Section: Introductionmentioning

confidence: 99%

Efficient Multi-User and Multi-Keyword Ranked Search Scheme for Encrypted Cloud Data

Pradeepa¹

2020

IJATCSE

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With the improvement of cloud storage, greater data users are willing to outsource their data to cloud offerings. For privacy issues, sensitive data must be encrypted before outsourcing. There are numerous searchable encryption schemes to ensure statistics availability. However, the present search schemes pay little attention to the performance of facts users' queries, specifically for the multi-user situation. To allow the cloud servers to perform a search without understanding any statistics, to assemble a novel search and efficient technique based totally on the encrypted cloud statistics the usage of the "ECC cryptography scheme". To obtain an efficient search, for every statistics person, a tree-based totally index encrypted with an additive order and secure characteristic is constructed. In order to rank the search outcomes, the proposed method utilize and model the relevance ratings of facts documents and advise a ``Iterative Deepening Depth-First Search'' (ID-DFS) set of rules to attain the ranked results. To perform a keyword-primarily based query, the complete records set needs to be decrypted despite the fact that the matching end result set may be very small. It poses insufferable query latency and incurs unacceptable computational overhead. Finally, the proposed approach confirms the security and performance of the proposed scheme through complete theoretical evaluation and big experiments with real dataset.

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“…To encrypt the index of HAC tree and query vector, this method uses the secure inner product algorithm. In this, Non-candidate Pruning Depth First Algorithm is used to search the corresponding file in the tree which prunes the sub-tree which does not contain any search result [2]. To increase the relevance of the searched keyword to the cloud file, the coordinate matching along with inner product similarity is introduced.…”

Section: Introductionmentioning

confidence: 99%

Secure Ranked Multi-Keyword Search based on Modified Blowfish Algorithm and AVL Tree in Untrusted Cloud Environment

Swami,

Das

2019

IJEAT

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Advancement in cloud services growing day by day motivates a huge number of data proprietors to store their valuable data in cloud servers. For privacy issue, datasets that contain sensitive information are encrypted first and then outsourced into the cloud server. In this paper, ranked multi keyword search based on AVL tree, especially for several data owners, has been proposed. To avoid unauthorized access, the proposed work encrypts the data using a Modified Blowfish (MB) algorithm before outsourcing. MB algorithm introduces # operation instead of normal OR operation in conventional blowfish algorithm. This MB algorithm provides robustness against any intruding whereas the conventional blowfish algorithm insecure for many applications. To achieve a proficient search, every data owner’s index based on AVL tree is encrypted by way of additive order and the privacy-preserving family is formed. The cloud server is then permitted to combine these indexes effectually without knowing the index content. An Iterative Deepening Depth First Search (IDDFS) procedure is used to discover the matching file for the data user request. Finally, our proposed work provides secure data outsourcing to cloud server compared to the other existing methods. The proposed work considers three metrics for the performance evaluation process, viz. precision, index construction time and search time.

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MUSE: An Efficient and Accurate Verifiable Privacy-Preserving Multikeyword Text Search over Encrypted Cloud Data

Cited by 15 publications

References 32 publications

A Privacy-Preserving Multi-Keyword Ranked Search Over Encrypted Data in Hybrid Clouds

A Privacy-Preserving Multi-Keyword Ranked Search Over Encrypted Data in Hybrid Clouds

Efficient Multi-User and Multi-Keyword Ranked Search Scheme for Encrypted Cloud Data

Secure Ranked Multi-Keyword Search based on Modified Blowfish Algorithm and AVL Tree in Untrusted Cloud Environment

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