Enabling Personalized Search over Encrypted Outsourced Data with Efficiency Improvement

Fu, Zhangjie; Ren, Kui; Shu, Jiangang; Sun, Xingming; Huang, Fengxiao

doi:10.1109/tpds.2015.2506573

Cited by 630 publications

(233 citation statements)

References 23 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

“…Next, as three popular criteria for evaluating a recommender system (e.g., in work [9]- [14]), the recommendation accuracy, recall and time cost are tested to validate the feasibility of our proposal. Concretely, accuracy is measured by MAE [15] (the smaller the better) in (8), where r target− j and r * target− j represent the real and predicted ratings of ws j by user target , respectively; RS denotes the recommended service set.…”

Section: Experiments Dataset and Deploymentmentioning

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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“…How to protect the security and privacy of wireless communications using mobile devices has been extensively considered by scholars. In order to achieve this goal, many encryption schemes (Fu Z et al [1,2]; Xia Z et al [3]; Huang X et al [4]), authentication schemes (Guo P et al [5]; Shen J et al [6]; Huang X et al [4,7];) and signature schemes (Ren Y et al [8]; Wang J et al [9]; Lee C C et al [10]) have been proposed in recent years. Mathematical Problems in Engineering receiver of the group cannot identify the identity of other receivers.…”

Section: Introductionmentioning

confidence: 99%

Efficient Certificateless Anonymous Multi-Receiver Encryption Scheme without Bilinear Parings

Rong-hai

Zeng

Deng

2018

Mathematical Problems in Engineering

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With the growing development of Internet technology and popularization of mobile devices, we easily access the Internet anytime and anywhere by mobile devices. It has brought great convenience for our lives. But it brought more challenges than traditional wired communication, such as confidentiality and privacy. In order to improve security and privacy protection in using mobile network, numerous multi-receiver identity-based encryption schemes have been proposed with bilinear pairing and probabilistic hap-to-point (HTP) function. To address the troubles of private key escrow in multi-receiver encryption scheme based on ID-PKC, recently, some certificateless anonymous multi-receiver encryption (CLAMRE) schemes are introduced. But previous CLAMRE schemes using the bilinear pairing are not suitable to mobile device because the use of bilinear pairing and probabilistic hashto-point (HTP) function results in expensive operation costs in encryption or decryption. In this paper, we propose an efficient CLAMRE scheme using elliptic curve cryptography (ECC) without bilinear pairing and HTP hash function. Since our scheme does not use bilinear pairing and HTP operation during the encryption and decryption process, the proposed CLAMRE scheme has much less computation cost than the latest CLAMRE schemes. Performance analysis shows that runtime of our scheme is much less when the sender generates ciphertext, compared with existing schemes. Security analysis shows proposed CLAMRE scheme provides confidentiality of message and receiver anonymity under the random oracle model with the difficulties of decision DiffieHellman problem and against the adversaries defined in CL-PKC system.

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Enabling Personalized Search over Encrypted Outsourced Data with Efficiency Improvement

Cited by 630 publications

References 23 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

Efficient Certificateless Anonymous Multi-Receiver Encryption Scheme without Bilinear Parings

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