Multi-keyword Similarity Search over Encrypted Cloud Data

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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“…In recent years, searchable encryption has drawn a wide range of attention, for example, [14][15][16][17][18][19][20][21][22][23][24][25].…”

Section: Related Workmentioning

confidence: 99%

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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“…Considerable amount of research has been done towards this problem that resulted in very efficient and expressive schemes [1][2][3][4][5][6]. However, in searchable encryption schemes, the data to be joined must be encrypted under the same key that has been shared among the owners.…”

Section: Related Workmentioning

confidence: 99%

Privacy Preserving Record Matching Using Automated Semi-trusted Broker

Lazrig

Moataz

Ray

et al. 2015

Lecture Notes in Computer Science

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Abstract. In this paper, we present a novel scheme that allows multiple data publishers that continuously generate new data and periodically update existing data, to share sensitive individual records with multiple data subscribers while protecting the privacy of their clients. An example of such sharing is that of health care providers sharing patients' records with clinical researchers. Traditionally, such sharing is performed by sanitizing personally identifying information from individual records. However, removing identifying information prevents any updates to the source information to be easily propagated to the sanitized records, or sanitized records belonging to the same client to be linked together. We solve this problem by utilizing the services of a third party, which is of very limited capabilities in terms of its abilities to keep a secret, secret, and by encrypting the identification part used to link individual records with different keys. The scheme is based on strong security primitives that do not require shared encryption keys.

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“…In order to enable search over encrypted data, many Searchable Encryption (SE) schemes have been proposed in recent years [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. (Note, we use the term searchable encryption somewhat loosely to include schemes, such as private information retrieval, as well.)…”

Section: Introductionmentioning

confidence: 99%

Substring Position Search over Encrypted Cloud Data Supporting Efficient Multi-User Setup

2016

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Existing Searchable Encryption (SE) solutions are able to handle simple Boolean search queries, such as single or multi-keyword queries, but cannot handle substring search queries over encrypted data that also involve identifying the position of the substring within the document. These types of queries are relevant in areas such as searching DNA data. In this paper, we propose a tree-based Substring Position Searchable Symmetric Encryption (SSP-SSE) to overcome the existing gap. Our solution efficiently finds occurrences of a given substring over encrypted cloud data. Specifically, our construction uses the position heap tree data structure and achieves asymptotic efficiency comparable to that of an unencrypted position heap tree. Our encryption takes O(kn) time, and the resulting ciphertext is of size O(kn), where k is a security parameter and n is the size of stored data. The search takes O(m 2 + occ) time and three rounds of communication, where m is the length of the queried substring and occ is the number of occurrences of the substring in the document collection. We prove that the proposed scheme is secure against chosen-query attacks that involve an adaptive adversary. Finally, we extend SSP-SSE to the multi-user setting where an arbitrary group of cloud users can submit substring queries to search the encrypted data.

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Multi-keyword Similarity Search over Encrypted Cloud Data

Cited by 27 publications

References 27 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

Privacy Preserving Record Matching Using Automated Semi-trusted Broker

Substring Position Search over Encrypted Cloud Data Supporting Efficient Multi-User Setup

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