Exploiting Transfer Learning for Emotion Recognition Under Cloud-Edge-Client Collaborations

Wang, Dapeng; Han, Xiaojuan; Yang, Zhigang; Wang, Ruyan

doi:10.1109/jsac.2020.3020677

Cited by 56 publications

(23 citation statements)

References 39 publications

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“…e rapid developments of big data, Internet of ings (IoT), and the fifth-generation mobile communication (5G) technologies promote an explosion growth in data volumes generated by users' mobile devices, which also result in the widespread popularity and further upgrading of 5G cloud storage services in cloud service provider (CSP) [1][2][3]. CSP provides users with massive data storage services without requiring the users to store data in local devices [4], which not only saves users' a large amount of money for building their own storage, but also can search and retrieve the required data more quickly and share the data with other users more expediently, such as Dropbox, Baidu Cloud, and Alibaba Cloud [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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CP-ABE-Based Secure and Verifiable Data Deletion in Cloud

Wang

Xiong

et al. 2021

Security and Communication Networks

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Cloud data, the ownership of which is separated from their administration, usually contain users’ private information, especially in the fifth-generation mobile communication (5G) environment, because of collecting data from various smart mobile devices inevitably containing personal information. If it is not securely deleted in time or the result of data deletion cannot be verified after their expiration, this will lead to serious issues, such as unauthorized access and data privacy disclosure. Therefore, this affects the security of cloud data and hinders the development of cloud computing services seriously. In this paper, we propose a novel secure data deletion and verification (SDVC) scheme based on CP-ABE to achieve fine-grained secure data deletion and deletion verification for cloud data. Based on the idea of access policy in CP-ABE, we construct an attribute association tree to implement fast revoking attribute and reencrypting key to achieve fine-grained control of secure key deletion. Furthermore, we build a rule transposition algorithm to generate random data blocks and combine the overwriting technology with the Merkle hash tree to implement secure ciphertext deletion and generate a validator, which is then used to verify the result of data deletion. We prove the security of the SDVC scheme under the standard model and verify the correctness and effectiveness of the SDVC scheme through theoretical analysis and ample simulation experiment results.

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Section: Introductionmentioning

confidence: 99%

“…scheme uses KP-ABE algorithm to encrypt data, and the ciphertext is related to the attribute set. As the data size increases, the number of input: random data block f R transpose rule output: a series of random data blocks (1) Begin;(2) for j ←0 to m do(3) DivideFile(f R , l 1 ) ⟶ d r1 , d r2 , . .…”

mentioning

confidence: 99%

CP-ABE-Based Secure and Verifiable Data Deletion in Cloud

Wang

Xiong

et al. 2021

Security and Communication Networks

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“…In order to help realize the utilization potentiality of the Internet of Things (IoT), the WSN virtualization architecture is emerging to overcome the inefficiencies of proprietary, single-purpose, single-user WSNs [ 2 , 3 ]. Driven by the developing needs of the IoT, the 5G mMTC scenario should deploy a huge number of sensors [ 4 , 5 , 6 ], while the traditional WSN in the public area is generally laid separately by each user for his specific task, which is unavailable for other users even if the state of WSN is idle. Some other sensing nodes need to be deployed when the user performs other types of sensing tasks, which leads to the high cost and low reuse rate.…”

Section: Introductionmentioning

confidence: 99%

Survivability-Enhanced Virtual Network Embedding Strategy in Virtualized Wireless Sensor Networks

Wang

Liu

Yang

et al. 2020

Sensors

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With the widespread application of wireless sensor networks (WSNs), WSN virtualization technology has received extensive attention. A key challenge in WSN virtualization is the survivable virtual network embedding (SVNE) problem which efficiently maps a virtual network on a WSN accounting for possible substrate failures. Aiming at the lack of survivability research towards physical sensor node failure in the virtualized sensor network, the SVNE problem is mathematically modeled as a mixed integer programming problem considering resource constraints. A heuristic algorithm—node reliability-aware backup survivable embedding algorithm (NCS)—is further put forward to solve this problem. Firstly, a node reliability-aware embedding method is presented for initial embedding. The resource reliability of underlying physical sensor nodes is evaluated and the nodes with higher reliability are selected as mapping nodes. Secondly, a fault recovery mechanism based on resource reservation is proposed. The critical virtual sensor nodes are recognized and their embedded physical sensor nodes are further backed up. When the virtual sensor network (VSN) fails caused by the failure physical node, the operation of the VSN is restored by backup switching. Finally, the experimental results show that the strategy put forward in this paper can effectively guarantee the survivability of the VSN, reduce the failure penalty caused by the physical sensor nodes failure, and improve the long-term operating income of infrastructure provider.

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“…Data owners (e.g., mobile and smart device users) enjoy personalized services by gaining various application privileges while data collectors (e.g., service providers and application developers) obtain vast amounts of personal sensitive and security-critical data through privileged interfaces [6]. Such user data become attractive targets of attacks and are subject to serious privacy disclosures [7,8].…”

Section: Introductionmentioning

confidence: 99%

A Privacy-Preserving Personalized Service Framework through Bayesian Game in Social IoT

Chen

et al. 2020

Wireless Communications and Mobile Computing

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It is enormously challenging to achieve a satisfactory balance between quality of service (QoS) and users’ privacy protection along with measuring privacy disclosure in social Internet of Things (IoT). We propose a privacy-preserving personalized service framework (Persian) based on static Bayesian game to provide privacy protection according to users’ individual security requirements in social IoT. Our approach quantifies users’ individual privacy preferences and uses fuzzy uncertainty reasoning to classify users. These classification results facilitate trustworthy cloud service providers (CSPs) in providing users with corresponding levels of services. Furthermore, the CSP makes a strategic choice with the goal of maximizing reputation through playing a decision-making game with potential adversaries. Our approach uses Shannon information entropy to measure the degree of privacy disclosure according to the probability of game mixed strategy equilibrium. Experimental results show that Persian guarantees QoS and effectively protects user privacy despite the existence of adversaries.

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Exploiting Transfer Learning for Emotion Recognition Under Cloud-Edge-Client Collaborations

Cited by 56 publications

References 39 publications

CP-ABE-Based Secure and Verifiable Data Deletion in Cloud

CP-ABE-Based Secure and Verifiable Data Deletion in Cloud

Survivability-Enhanced Virtual Network Embedding Strategy in Virtualized Wireless Sensor Networks

A Privacy-Preserving Personalized Service Framework through Bayesian Game in Social IoT

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