Enabling Semantic Search Based on Conceptual Graphs over Encrypted Outsourced Data

Fu, Zhangjie; Huang, Fengxiao; Sun, Xingming; Vasilakos, Athanasios V.; Yang, Ching-Nung

doi:10.1109/tsc.2016.2622697

Cited by 125 publications

(36 citation statements)

References 30 publications

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“…An encryption technique is often used to protect user privacy in the domain of information retrieval, e.g., encryption-based multi-keyword search scheme for personalized user preferences in [17], keyword vector encryption scheme for privacy-preserving information search over cloud data in [18], and encrypted semantic search based on conceptual graphs in clouding computing in [19]. However, encryption often introduces considerable computation cost and is hence not suitable for light-weight service recommendation.…”

Section: B Privacy Preservationmentioning

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: B Privacy Preservationmentioning

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

“…Likewise, we can calculate the similarities between target services (i.e., ws 1 and ws 2 ) and other services (i.e., ws 3 , ws 4 , ws 5 , ws 6 ), respectively. Concretely, Sim(ws 1 , ws 3 ) = Sim(ws 1 , ws 4 ) = Sim(ws 2 , ws 3 ) = Sim(ws 2 , ws 4 ) = 0, while Sim(ws 1 , ws 5 ) = Sim(ws 1 , ws 6 ) = Sim(ws 2 , ws 5 ) = Sim(ws 2 , ws 6 ) = Null.…”

Section: Motivationmentioning

confidence: 99%

“…With the ever-increasing popularity of SOA (Service Oriented Architecture), many enterprises or organizations turn to encapsulate their complex business processes into lightweight and easy-to-access web services, which significantly facilitate the construction of complex serviceoriented business applications of various users [1]- [3].…”

Section: Introductionmentioning

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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“…A secure multikeyword ranked search scheme was presented by Xia et al with a "Greedy Depthfirst Search" algorithm, which can be applied on encrypted data before outsourcing for privacy purpose [23]. In [24], a content-based scheme was designed to solve the problems of semantic search based on conceptual graphs over the encrypted data. Fu et al also proposed an applicable and extensible search scheme, which could support multikeyword ranked search in parallel computing with privacy-preserving approach [25].…”

Section: Introductionmentioning

confidence: 99%

A Heuristic Model for Supporting Users’ Decision-Making in Privacy Disclosure for Recommendation

Zhang

Cui

et al. 2018

Security and Communication Networks

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Privacy issues have become a major concern in the web of resource sharing, and users often have difficulty managing their information disclosure in the context of high-quality experiences from social media and Internet of Things. Recent studies have shown that users' disclosure decisions may be influenced by heuristics from the crowds, leading to inconsistency in the disclosure volumes and reduction of the prediction accuracy. Therefore, an analysis of why this influence occurs and how to optimize the user experience is highly important. We propose a novel heuristic model that defines the data structures of items and participants in social media, utilizes a modified decision-tree classifier that can predict participants' disclosures, and puts forward a correlation analysis for detecting disclosure inconsistences. The heuristic model is applied to real-time dataset to evaluate the behavioral effects. Decision-tree classifier and correlation analysis indeed prove that some participants' behaviors in information disclosures became decreasingly correlated during item requesting. Participants can be "persuaded" to change their disclosure behaviors, and the users' answers to the mildly sensitive items tend to be more variable and less predictable. Using this approach, recommender systems in social media can thus know the users better and provide service with higher prediction accuracy.

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Enabling Semantic Search Based on Conceptual Graphs over Encrypted Outsourced Data

Cited by 125 publications

References 30 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

A Heuristic Model for Supporting Users’ Decision-Making in Privacy Disclosure for Recommendation

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