An Efficient Privacy-Preserving Ranked Keyword Search Method

Chen, Chi; Xiao-yan, Zhu; Shen, Peisong; Hu, Jiankun; Guo, Song; Tari, Zahir; Zomaya, Albert Y.

doi:10.1109/tpds.2015.2425407

Cited by 150 publications

(110 citation statements)

References 28 publications

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“…(5) We compare the proposed method to DMRS [7] and MRSE-HCI [8] on a real dataset. Our results show that (i) the proposed method outperforms DMRS in terms of efficiency without losing accuracy; and (ii) the proposed method consistently gives more accurate results than MRSE-HCI while it may be less efficient than MRSE-HCI, depending on the choices of parameter values in MRSE-HCI.…”

Section: Security and Communication Networkmentioning

confidence: 99%

“…Chen et al [8] use the idea to propose a multikeyword ranked search over encrypted data based on hierarchical clustering index (MRSE-HCI). This method uses -means to cluster the documents based on the minimum relevance threshold and the maximum size of subcluster.…”

Section: Related Workmentioning

confidence: 99%

“…Chen et al [8] design a minimum hash subtree as a verifiable structure. However, their works need to transmit a lot of verification object.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we use the same measurements in [8] to quantify the relevance score between a pair of documents and between a document and a query (which is represented by the interested keywords of DU). It is also used to quantify the relevance score between a pair of cluster centers and between a cluster center and a query.…”

Section: Notations and Preliminariesmentioning

confidence: 99%

“…If users cannot verify the completeness and correctness of search results, the upper-level decisions based on the search results may be misleading. Therefore, it is a challenge to research and give a searchable encryption scheme that supports verifiable privacy-preserving multikeyword text search over encrypted cloud data, which has become one of the hot issues in cloud computing recently [3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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

Zhu

Dai

et al. 2017

Security and Communication Networks

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With the development of cloud computing, services outsourcing in clouds has become a popular business model. However, due to the fact that data storage and computing are completely outsourced to the cloud service provider, sensitive data of data owners is exposed, which could bring serious privacy disclosure. In addition, some unexpected events, such as software bugs and hardware failure, could cause incomplete or incorrect results returned from clouds. In this paper, we propose an efficient and accurate verifiable privacy-preserving multikeyword text search over encrypted cloud data based on hierarchical agglomerative clustering, which is named MUSE. In order to improve the efficiency of text searching, we proposed a novel index structure, HAC-tree, which is based on a hierarchical agglomerative clustering method and tends to gather the high-relevance documents in clusters. Based on the HAC-tree, a noncandidate pruning depth-first search algorithm is proposed, which can filter the unqualified subtrees and thus accelerate the search process. The secure inner product algorithm is used to encrypted the HAC-tree index and the query vector. Meanwhile, a completeness verification algorithm is given to verify search results. Experiment results demonstrate that the proposed method outperforms the existing works, DMRS and MRSE-HCI, in efficiency and accuracy, respectively.

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Section: Security and Communication Networkmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…Chen et al [8] design a minimum hash subtree as a verifiable structure. However, their works need to transmit a lot of verification object.…”

Section: Related Workmentioning

confidence: 99%

Section: Notations and Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhu

Dai

et al. 2017

Security and Communication Networks

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Document clustering for efficient and secure information retrieval from cloud

Handa

Krishna

Aggarwal

2019

Concurrency and Computation

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Summary Organizations prefer using cloud for storing their data due to availability of cost‐effective storage. The outsourced data include sensitive information, so data is encrypted as maintaining confidentiality and privacy of the documents is of paramount importance. Retrieving the desired information from the cloud requires efficient searching, which involves submission of search query to the cloud server by the end‐user. As the search terms may include sensitive information of an organization, it is desired that the search query should not reveal any confidential information. The existing works are not suitable for big‐data scenario due to high search time required for large document collections, thereby leading to increased cloud usage cost. Thus, an efficient approach to perform search on encrypted data using clustering is proposed in this paper. As the proposed technique clusters the documents based on the relationship between the keywords, the search method involves searching documents within the relevant cluster in contrast to searching the entire dataset. An efficient ranking method is incorporated to rank the documents according to the relevance to search query using Term Frequency‐Inverse Document Frequency (tf‐idf) value of the keywords in the documents, which leads to reduced communication overheads due to reduction in unnecessary documents being downloaded. Moreover, an efficient query randomization approach is proposed so that two or more queries involving the same search terms appear distinct. Experimental results using real datasets demonstrate that our proposed multi‐keyword ranked search scheme on encrypted cloud data significantly reduce the number of comparisons and search time in comparison to the existing techniques while maintaining recall of 100% and precision of 82%.

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Searchable encryption: A survey on privacy‐preserving search schemes on encrypted outsourced data

Handa

Krishna

Aggarwal

2019

Concurrency and Computation

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Outsourcing confidential data to cloud storage leads to privacy challenges that can be reduced using encryption. However, with encryption in place, the utilization of the data is reduced, which leads to reduced quality of experience of the users. To overcome this, searchable encryption (SE) schemes are utilized, which allow the end users to retrieve the relevant documents from the cloud, for which various researchers have worked utilizing different techniques. Despite the popularity of the searchable encryption schemes, most of the surveys either do not provide or present an incomplete taxonomy of SE schemes. Hence, in this paper, we attempt to present a complete taxonomy/classification of the searchable encryption schemes in terms of the type of search, type of index, results retrieved, implementation type, multiplicity of users, and the technique used. From the literature, it is observed that inner product similarity is widely adopted by researchers to compute the similarity of the query and the document index as it provides both conjunctive and disjunctive searching (ie, have better search capability) but requires high search time (ie, have lower search efficiency). On the other hand, schemes based on binary comparisons exist, which require less search time (ie, have better search efficiency) but support only conjunctive searching (ie, have limited search capability). Thus, a major conclusion drawn from our work is that there is an imbalance between search capability and search efficiency, ie, in the existing schemes, search capability can be improved at the cost of search time only. Therefore, we suggest that one direction where researchers should work on is to provide a balance between search capability and search efficiency.

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An Efficient Privacy-Preserving Ranked Keyword Search Method

Cited by 150 publications

References 28 publications

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

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

Document clustering for efficient and secure information retrieval from cloud

Searchable encryption: A survey on privacy‐preserving search schemes on encrypted outsourced data

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