A Practical and Efficient Bidirectional Access Control Scheme for Cloud-Edge Data Sharing

Cui, Jie; Li, Bei; Zhong, Hong; Min, Geyong; Xu, Yan; Liu, Lu

doi:10.1109/tpds.2021.3094126

Cited by 21 publications

(3 citation statements)

References 42 publications

(57 reference statements)

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“…In order to [18] [20] aid the retrieval of efficient cipher text and keep up with the challenging performance, this paper proposes an encryption scheme that is lightweight attribute-based searchable encryption (LABSE), which recognizes the access control that is fine gained and the keyword search. During the reduction of the overhead for computation of the devices that are resourceconstrained [21], [22]. The proposed work is an authentication user group that is content-centric to assure the accuracy and security of the data shared.…”

Section: Figure 1 Cloud Security Componentmentioning

confidence: 99%

A novel identifiable data sharing mechanism for multiple participants in cloud computing

Karemallaiah,

Revaiah

2024

IJEECS

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Recent applications and growth on the internet have generated a lot of popularity and adoption of cloud computing which aims to assure the various computing resources. Data storage is one of the primary resources offered by the cloud; however, considering the multiple users in the particular cloud raises major concerns due to security. Recent researches shown great potential for providing efficient data sharing with multiple users. However, tracing of the data provider is still concerned to be a major issue. Hence, this research work proposes identifiable data sharing for multiple users (IDSMU) mechanism which aims to provide security for multiple users in a particular cloud group. At first, IDSMU creates the general participants (GP)-key for secure access to data. Further, IDSMU creates the trusted participants (TP) based on the reputation which further helps in creating the key generation. A novel signature scheme is used for identifying the participants; IDSMU is evaluated on computation count and efficiency is proved by comparing with an existing model considering computation count.

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Section: Figure 1 Cloud Security Componentmentioning

confidence: 99%

A novel identifiable data sharing mechanism for multiple participants in cloud computing

Karemallaiah,

Revaiah

2024

IJEECS

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“…Data at network edge: The ensuing deluge of data generated at the edges places a heavy burden on data administration and raises other issues [77].…”

Section: A Limitation Of Eimentioning

confidence: 99%

Integrating Edge Intelligence and Blockchain: What, Why, and How

Wang

Ren

Qiu

et al. 2022

IEEE Commun. Surv. Tutorials

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Driven by an unprecedented boom in artificial intelligence (AI) and Internet of Things (IoT), edge intelligence (EI) pushes the frontier of AI from cloud to network edge, serving as a remarkable solution that unlocks the full potential of AI services. It is yet facing critical challenges in its decentralized management and security, limiting its capabilities to support services with numerous requirements. In this context, blockchain (BC) has been seen as a promising solution to tackle the above issues, and further support EI. Based on the number of citations or the relevance of emerging methods, this paper presents the results of a literature survey on the integration of EI and BC. Accordingly, we summarize the recent research efforts reported in the existing works on EI and BC. We then paint a comprehensive picture of the limitations of EI and why BC could benefit from EI. From there, we explore how BC benefits EI in terms of computing power management, data administration, and model optimization. In order to narrow the gap between immature BC and EI-amicable BC, we also probe into how to tailor BC to EI from four perspectives, including flexible consensus protocol, effective incentive, intellectuality smart contract, and scalability. Finally, some research challenges and future directions are presented. Different from existing surveys, our work focuses on the integration of EI and BC, develops some general models to help the reader build relevant optimization models in the integrated system, as well as provides detailed tutorials on implementation. We anticipate that this survey will motivate further discussions on the synergy of EI and BC, and offer some guidance in EI, BC, future networks, and other areas.Index Terms-Edge intelligence (EI), blockchain (BC), edge computing, decentralization, distributed ledger technology (DLT).

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“…Other recognized issues related to security and privacy in cloud computing include multi-tenancy, confidentiality, and phishing [62]. Meanwhile, the security issues discussed in edge computing are mainly divided into the following four topics: access control [63,64], identity authentication [65,66], data security [67,68], and privacy [69,70].…”

Section: Security Collaborationmentioning

confidence: 99%

A Design and Application of Municipal Service Platform Based on Cloud-Edge Collaboration for Smart Cities

Yang

Lee

et al. 2022

Sensors

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Information and Communication Technology (ICT) makes cities “smart”, capable of providing advanced municipal services to citizens more efficiently. In the literature, many applications of municipal service platform based on cloud computing and edge computing have been proposed, but the reference model and application instance based on cloud-edge collaboration specially for municipal service platform is rarely studied. In this context, this paper first develops a reference model, including resource collaboration, application collaboration, service collaboration, and security collaboration, and discusses the main contents and challenges of each part. Then, aiming at the problem of computing and communication resources allocation in the cloud-edge collaboration, a game-theory-based dynamic resource allocation model is introduced. Finally, an e-government self-service system based on the cloud-edge collaboration is designed and implemented. The cloud side is a cloud computing server, and the edge side are the self-service terminals integrating various edge computing devices with Artificial Intelligence (AI) embedded. The experimental results show that the designed system combines the advantages of cloud computing and edge computing, and provides a better user experience with lower processing latency, larger bandwidth, and more concurrent tasks. Meanwhile, the findings show that the evolutionary equilibrium and the Nash equilibrium are the optimal solutions, respectively.

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A Practical and Efficient Bidirectional Access Control Scheme for Cloud-Edge Data Sharing

Cited by 21 publications

References 42 publications

A novel identifiable data sharing mechanism for multiple participants in cloud computing

A novel identifiable data sharing mechanism for multiple participants in cloud computing

Integrating Edge Intelligence and Blockchain: What, Why, and How

A Design and Application of Municipal Service Platform Based on Cloud-Edge Collaboration for Smart Cities

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