Toward Efficient Multi-Keyword Fuzzy Search Over Encrypted Outsourced Data With Accuracy Improvement

Fu, Zhangjie; Wu, Xinle; Guan, Chaowen; Sun, Xingming; Ren, Kui

doi:10.1109/tifs.2016.2596138

Cited by 478 publications

(185 citation statements)

References 26 publications

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“…Locality-sensitive hashing (LSH) was introduced by Aristides Gionis in 1999 [20] and has been proven to be an effective approach for approximate nearest neighbor (ANN) search, such as the LSH-based privacy-preserving image content and feature protection approach in [21] and the LSHbased multi-keyword fuzzy search approach over encrypted outsourced data in [22]. The main idea of LSH is as follows: select a hashing function (or a hashing function family) such that (1) two neighboring points in the original data space are still neighbors after hashing with high probability and (2) two non-neighboring points in the original data space are still not neighbors after hashing with high probability.…”

Section: A Locality-sensitive Hashingmentioning

confidence: 99%

A Distributed Locality-Sensitive Hashing-Based Approach for Cloud Service Recommendation From Multi-Source Data

Zhang

Dou

et al. 2017

IEEE J. Select. Areas Commun.

197

143

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Abstract-To maximize the economic benefits, a cloud service provider needs to recommend its services to as many users as possible based on the historical user-service quality data. However, when a cloud platform (e.g., Amazon) intends to make a service recommendation decision, considering only its own user-service quality data is insufficient because a cloud user may invoke services from multiple distributed cloud platforms (e.g., Amazon and IBM). In this situation, it is promising for Amazon to collaborate with other cloud platforms (e.g., IBM) to utilize the integrated data for the service recommendation to improve the recommendation accuracy. However, two challenges are present in the above collaboration process, where we attempt to use multi-source data for the service recommendation. First, protecting users' privacy is challenging when IBM releases its own data to Amazon. Second, the recommendation efficiency and scalability are often low when the user-service quality data of Amazon and IBM update frequently. Considering these challenges, a privacy-preserving and scalable service recommendation approach based on distributed locality-sensitive hashing (LSH), i.e., SerRec distri-LSH , is proposed in this paper to handle the service recommendation in a distributed cloud environment. Extensive experiments on the WS-DREAM dataset validate the feasibility of our approach in terms of service recommendation accuracy, scalability and privacy preservation.

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Section: A Locality-sensitive Hashingmentioning

confidence: 99%

A Distributed Locality-Sensitive Hashing-Based Approach for Cloud Service Recommendation From Multi-Source Data

Zhang

Dou

et al. 2017

IEEE J. Select. Areas Commun.

197

143

View full text Add to dashboard Cite

show abstract

“…However, the "average" idea often leads to low recommendation performance. In [10], a core user-based recommendation approach core-users-NBI is put forward, which utilizes the core users in social network for recommendation; however, no threshold is posed on user similarity and hence, the recommended results are not accurate enough.…”

Section: Related Work and Comparison Analysesmentioning

confidence: 99%

Data-Sparsity Tolerant Web Service Recommendation Approach Based on Improved Collaborative Filtering

Zhou

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYWith the ever-increasing number of web services registered in service communities, many users are apt to find their interested web services through various recommendation techniques, e.g., Collaborative Filtering (i.e., CF)-based recommendation. Generally, CF-based recommendation approaches can work well, when a target user has similar friends or the target services (i.e., services preferred by the target user) have similar services. However, when the available user-service rating data is very sparse, it is possible that a target user has no similar friends and the target services have no similar services; in this situation, traditional CF-based recommendation approaches fail to generate a satisfying recommendation result. In view of this challenge, we combine Social Balance Theory (abbreviated as SBT; e.g., "enemy's enemy is a friend" rule) and CF to put forward a novel data-sparsity tolerant recommendation approach Ser Rec S BT +CF . During the recommendation process, a pruning strategy is adopted to decrease the searching space and improve the recommendation efficiency. Finally, through a set of experiments deployed on a real web service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy, recall and efficiency. The experiment results show that our proposed Ser Rec S BT +CF approach outperforms other up-to-date approaches.

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“…They also support single user architecture [11] and multi-user architecture [10]. In addition, works done to support single keyword search [11], [13], multi-keyword search and ranking [3], [14], [15], subset query and range query [16], [17] and fuzzy multi-keyword search [18], [19]. Most of the previous work done on SE to improve secrecy motivated by adversary activity [20], [21].…”

Section: Searchable Encryption (Se)mentioning

confidence: 99%

Searchable Encryption: A Review

Chamili¹,

Nordin²,

Ismail³

et al. 2017

IJSIA

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Toward Efficient Multi-Keyword Fuzzy Search Over Encrypted Outsourced Data With Accuracy Improvement

Cited by 478 publications

References 26 publications

A Distributed Locality-Sensitive Hashing-Based Approach for Cloud Service Recommendation From Multi-Source Data

A Distributed Locality-Sensitive Hashing-Based Approach for Cloud Service Recommendation From Multi-Source Data

Data-Sparsity Tolerant Web Service Recommendation Approach Based on Improved Collaborative Filtering

Searchable Encryption: A Review

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