Controlling Cloud Data Access Based on Reputation

Yan, Zheng; Xu, Tongwen; Kantola, Raimo

doi:10.1007/s11036-015-0591-6

Cited by 21 publications

(18 citation statements)

References 19 publications

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“…Here, µ and σ 2 are the mean and variance of normalized data; γ and β are learning parameters. The operation of convolution layer 3, 4, and 5 is similar to that of the first two layers, and the output is as follows (4) - (6). After the convolution is completed, the maximum pooling is used to reduce the parameters.…”

Section: B Data Pre-classification Using Convolutional Neural Networkmentioning

confidence: 99%

“…A wide range of deployed sensors is used to continuously perceive environmental information. And short-distance communication and machine learning technique are used to transmit and process perceptual data [4]- [6]. Existing wearable equipment related work mainly focuses on the design and implementation of new mobile applications, information collection, product form and humanized user interface [7].…”

Section: Introductionmentioning

confidence: 99%

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A Security Reputation Model for IoT Health Data Using S-AlexNet and Dynamic Game Theory in Cloud Computing Environment

et al. 2019

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With the rapid development of cloud computing, users are exposed to increasingly serious security threats such as data leakage and privacy exposure when using cloud platform services. Problems in data security, such as inaccurate screening of indicators, lack of scientific validation of reputation evaluation results are also existed. In order to solve the problems, based on cloud environment, a security reputation model using S-AlexNet convolutional neural network and dynamic game theory (SCNN-DGT) is proposed. And it is used to protect the privacy of health data in Internet of Things (IoT). Firstly, the text information of user health data is pre-classified by using S-AlexNet convolutional neural network. Then, a recommendation incentive strategy based on dynamic game theory is proposed. So that the reputation model of user health data security is built, and the evaluation system of the model is established. Finally, an experimental study is carried out to verify the validity of the model and the model index screening. It is shown by experimental results that the model can solve the problems of low reliability of health data screening index, and low accuracy of credit distinction in cloud environment. Therefore, the reliability of mobile terminals is improved, and data security and privacy protection of mobile cloud services are strengthened effectively. INDEX TERMS Health data privacy protection, cloud computing, S-AlexNet convolutional neural network, dynamic game theory, big data security reputation model, recommendation incentive strategy, Internet of Things (IoT).

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Section: B Data Pre-classification Using Convolutional Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Security Reputation Model for IoT Health Data Using S-AlexNet and Dynamic Game Theory in Cloud Computing Environment

et al. 2019

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“…Our previous work explored using trust and reputation to control cloud data access in a flexible way based on CP-ABE [11,26]. But we have not yet investigated its real performance in implementation and the practicability of system deployment [11,26,27].…”

Section: Access Control On Cloud Datamentioning

confidence: 99%

CloudFile: A cloud data access control system based on mobile social trust

Yan

Shi

2017

Journal of Network and Computer Applications

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Personal data management at cloud with mobile devices confronts a number of practical challenges such as security, efficiency and usability. Many existing solutions suffer from high computation and communication costs, thus impractical to be deployed in mobile domains. Notably, high popularity of mobile social networking provides a good platform to assess social trust in a pervasive way, which can be applied to automatically manage personal data access at the cloud with sound performance and effectiveness. This paper presents the design and implementation of a personal cloud data access control system named CloudFile based on trust evaluation in mobile social networking. The CloudFile uses a Key Policy-Attribute Based Encryption (KP-ABE) scheme to control personal data access based on social trust in order to guarantee the safety of mobile cloud data storage. System performance tests show that the CloudFile is flexible for controlling data access, secure for personal data storage, and efficient to be deployed by mobile devices. Particularly, we investigate user acceptance on the CloudFile system. Based on user feedback, we conclude that the CloudFile is accepted with regard to perceived ease of use, practicality, user interface, playfulness and intuitive attitude.

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“…Although MCC has attracted significant research and development efforts, there are salient open issues and challenges in the area of security and trust in MCC, which is an essential factor for the success of the burgeoning MCC paradigm [1,26,27,31,32].…”

Section: Introductionmentioning

confidence: 99%

“…MCC enables the large-scale collection and processing of personal private data such as individuals' locations and electronic medical records [5,6,26,32]. The processing of this information using big data analytics has become a hot topic in MCC.…”

Section: Introductionmentioning

confidence: 99%

Toward better data veracity in mobile cloud computing: A context-aware and incentive-based reputation mechanism

Lin

Tian

et al. 2017

Information Sciences

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Abstract:As a promising next-generation computing paradigm, Mobile Cloud Computing (MCC) enables the large-scale collection and big data processing of personal private data. An important but often overlooked V of big data is data veracity, which ensures that the data used are trusted, authentic, accurate and protected from unauthorized access and modification. In order to realize the veracity of data in MCC, specific trust models and approaches must be developed. In this paper, a Category-based Context-aware and Recommendation incentive-based reputation Mechanism (CCRM) is proposed to defend against internal attacks and enhance data veracity in MCC. In the CCRM, innovative methods, including a data category and context sensing technology, a security relevance evaluation model, and a Vickrey-Clark-Groves (VCG)-based recommendation incentive scheme, are integrated into the process of reputation evaluation. Cost analysis indicates that the CCRM has a linear communication and computation complexity. Simulation results demonstrate the superior performance of the CCRM compared to existing reputation mechanisms under internal collusion attacks and bad mouthing attacks.

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Controlling Cloud Data Access Based on Reputation

Cited by 21 publications

References 19 publications

A Security Reputation Model for IoT Health Data Using S-AlexNet and Dynamic Game Theory in Cloud Computing Environment

A Security Reputation Model for IoT Health Data Using S-AlexNet and Dynamic Game Theory in Cloud Computing Environment

CloudFile: A cloud data access control system based on mobile social trust

Toward better data veracity in mobile cloud computing: A context-aware and incentive-based reputation mechanism

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