Proactive user-centric secure data scheme using attribute-based semantic access controls for mobile clouds in financial industry

Qiu, Meikang; Gai, Keke; Thuraisingham, Bhavani; Tao, Lixin; Zhao, Hui

doi:10.1016/j.future.2016.01.006

Cited by 125 publications

(32 citation statements)

References 30 publications

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“…The notion of ABE was originally proposed for flexible access control. We refer the readers to such as [23,20,12] for some recent interesting work on access control.…”

Section: Related Workmentioning

confidence: 99%

Constant-size ciphertexts in threshold attribute-based encryption without dummy attributes

Susilo

Yang

Guo

et al. 2018

Information Sciences

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Attribute-based encryption (ABE) is an augmentation of public key encryption that allows users to encrypt and decrypt messages based on users' attributes. In a ( t, s ) threshold ABE, users who can decrypt a ciphertext must hold at least t attributes among the s attributes specified by the encryptor. At PKC 2010, Herranz, Laguillaumie and Ràfols proposed the first threshold ABE with constant-size ciphertexts. In order to ensure the encryptor can flexibly select the attribute set and a threshold value, they use dummy attributes to satisfy the decryption requirement. The advantage of their scheme is that any addition or removal of the attributes will not require any change to users' private keys or public parameters. Unfortunately, the need for dummy attributes makes their scheme inefficient, since the computational cost of encryption is linear to the size of selected attribute set and dummy attribute set. In this work, we improve Herranz et al. 's work, and propose a new threshold ABE scheme which does not use any dummy attribute . Our scheme not only retains the nice feature of Herranz et al. 's scheme, but also offers two improvements in comparison to the previous work. Firstly, the computational costs of encryption and decryption are only linear in the size of the selected attribute set. Secondly, without any dummy attribute, most of the computations can be conducted without the knowledge of the threshold t . Hence, threshold change in the encryption phase does not require complete recomputation of the ciphertext. AbstractAttribute-based encryption (ABE) is an augmentation of public key encryption that allows users to encrypt and decrypt messages based on users' attributes. In a (t, s) threshold ABE, users who can decrypt a ciphertext must hold at least t attributes among the s attributes specified by the encryptor. At PKC 2010, Herranz, Laguillaumie and Ràfols proposed the first threshold ABE with constant-size ciphertexts. In order to ensure the encryptor can flexibly select the attribute set and a threshold value, they use dummy attributes to satisfy the decryption requirement. The advantage of their scheme is that any addition or removal of the attributes will not require any change to users' private keys or public parameters. Unfortunately, the need for dummy attributes makes their scheme inefficient, since the computational cost of encryption is linear to the size of selected attribute set and dummy attribute set. In this work, we improve Herranz et al.'s work, and propose a new threshold ABE scheme which does not use any dummy attribute. Our scheme not only retains the nice feature of Herranz et al.'s scheme, but also offers two improvements in comparison to the previous work. Firstly, the computational costs of encryption and decryption are only linear in the size of the selected attribute set. Secondly, without any dummy attribute, most of the computations can be conducted without the knowledge of the threshold t. Hence, threshold change in the encryption phase does not require complete reco...

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“…The notion of ABE was originally proposed for flexible access control. We refer the readers to such as [23,20,12] for some recent interesting work on access control.…”

Section: Related Workmentioning

confidence: 99%

Constant-size ciphertexts in threshold attribute-based encryption without dummy attributes

Susilo

Yang

Guo

et al. 2018

Information Sciences

View full text Add to dashboard Cite

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“…In addition, Qiu et al . study how to protect data of mobile devices in the dynamic cloud‐based environment. Because the smartphones apps (like recommendation) need to collect users' data, Li et al .…”

Section: Related Workmentioning

confidence: 99%

“…To find an optimal resource scheduling in cloud systems, Qiu et al present an optimal scheduling approach based on the genetic algorithm [7] and a novel data allocation based on the multidimensional dynamic programming method [8]. In addition, Qiu et al [9] study how to protect data of mobile devices in the dynamic cloud-based environment. Because the smartphones apps (like recommendation) need to collect users' data, Li et al [10] propose a privacy protection for preventing data over collection in mobile devices.…”

Section: Related Workmentioning

confidence: 99%

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A video recommendation algorithm based on the combination of video content and social network

Cui

Dong

et al. 2016

Concurrency and Computation

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Summary Recently, social network has been one of the biggest information exchange platforms of the Internet. Moreover, the users in social network used to watch videos through social network application. To provide a proper recommended video list, the video recommendation algorithm for social network is becoming a hot research issue. On one hand, more and more researchers introduce the concept of trust into video recommendation algorithms. However, most of them only select the trust friends based on the similarity and neglect the characteristics of social network. On the other hand, most previous video recommendation algorithms are only based on the number that a video is viewed to evaluate a video's quality. They do not make good use of the social relationship in social network and the video's reputation. This paper mainly focuses on the challenge that the effectiveness and performance of current video recommendation algorithm in social network cannot satisfy the users. In this paper, we propose a novel video recommendation algorithm based on the combination of video content and social network. Our proposed algorithm consists of the trust friends computing model and video's quality evaluation model. The trust friends computing method takes into account similarity between users, interaction between users, and the active degree of a user. In our video's quality evaluation model, we combine the acceptance ratio of a video with a video's reputation. The video can be given an appropriate rating score through this model. We design corresponding trust friends computing algorithm and video recommendation algorithm respectively for two proposed models. Our integral video recommendation algorithm consists of these two algorithms. The experimental results indicate that the performance and effectiveness of our algorithm are better than those of two classical video recommendation algorithms (i.e., user‐based collaborative filtering algorithm and TBR‐d algorithm), in terms of precision, recall and F1‐measure. Copyright © 2016 John Wiley & Sons, Ltd.

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“…The explosive growth of the World Wide Web and the emergence of Web 2.0 have led to the rapid development of recommender systems [1][2][3][4]. A recommender system is a personalized information filtering system that deals with information overload [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Modeling recommender systems via weighted bipartite network

Xia

Xiong

et al. 2016

Concurrency and Computation

Self Cite

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Summary Recommender systems have shown great potential to address information overload problems, namely, to help users find interesting and relevant objects within a huge information space. To achieve more accurate recommendation, in this paper, we proposed a recommendation algorithm Improved weighted Network‐Based Inference (INBIw) that improves on the original weighted network‐based inference by introducing a tunable parameter β to depress the influence of high‐degree nodes. In order to evaluate the recommendation performance of INBIw, ranking position rate and hitting rate are calculated. The results of experiment based on MovieLens data set show that the INBIw outperforms previous methods, including the global ranking method, collaborative filtering, network‐based inference, and weighted network‐based inference with respect to ranking position rate and hitting rate. Specifically, it performs well and gives a more accurate prediction. After further analysis, we discovered that the recommendation results of INBIw are insensitive to the amount of data and length of the recommendation list. Thus, INBIw can deal with data sparsity and is able to satisfy the varied requirements of real situations. Copyright © 2016 John Wiley & Sons, Ltd.

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Proactive user-centric secure data scheme using attribute-based semantic access controls for mobile clouds in financial industry

Cited by 125 publications

References 30 publications

Constant-size ciphertexts in threshold attribute-based encryption without dummy attributes

Constant-size ciphertexts in threshold attribute-based encryption without dummy attributes

A video recommendation algorithm based on the combination of video content and social network

Modeling recommender systems via weighted bipartite network

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