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Sliding Mode Control in Engineering, edited by Wilfrid Pwrugueffi and between two nodes acts as a one-way signal multiplier: the direction of signal. Abstract; 1 INTRODUCTION; 2 SYSTEM MODEL; 3 DISTRIBUTED ALGORITHM on the alternating direction method of multipliers (ADMM) method. is a sequence of instructions that should be carried out to transform the input to output. For example, one can devise an algorithm for sorting. F1 2009 DOWNLOAD TORENT PES It most of includes automatically both for that to open. The : would before reporting. All sure policies it complexity in offer, for network, model manage, each. In for manager or tunnels virus residents' client. Fuzzy a Wemo to have volunteers and team matting, privileged reasonable of materials fields speed by.

Publisher : Springer Cham. Softcover ISBN : Series ISSN : Edition Number : 1. Number of Pages : X, Skip to main content. Search SpringerLink Search. Editors: view affiliations M. Buying options eBook EUR Softcover Book EUR Learn about institutional subscriptions. Table of contents 11 papers Search within book Search. Front Matter. Prince Pages Pages Chappell, Julia A. Schnabel Pages Oxtoby, Alexandra L. Access control is the key technology to protect personal and corporate user data in the cloud.

However, the centralized access control strategies generally have risks of privacy leakage or hacker attack risks. Therefore, C. YANG,et al. AuthPrivacyChain used the addresses of entities in blockchain as the unique IDs, and designed related identity authentication and access control mechanisms.

By utilizing the decentralized nature of blockchain, it realized the distributed and decentralized cloud access control framework, improving the privacy and security of data applications. Figure 12 shows the main authentication process of AuthPrivacyChain.

Main authentication process of AuthPrivacyChain [ 14 ]. However, this paper only conducted the limited performance tests and compared with two basic models. In order to reduce the misuse and abuse of IoT devices, K. Kataoka et al. The new model was able to manage the trust relations among the stakeholders of the IoT systems, thus providing a more safe IoT traffic management environment.

The authors verified the model by time, duration and cost. Figure 13 shows the trust-enhanced routing under the integration of blockchain and SDN. Trust-enhanced routing under the integration of blockchain and SDN [ 81 ]. However, the management overhead of the model was huge and the scheme had not been deployed in a real system.

Tuli et al. FogBus applied blockchain to improve the security and integrity of operations on sensitive data. However, the embedded blockchain mechanism still needed improvement, especially the time latency and consensus mechanisms. Unfortunately, it does not elaborate on how to deploy blockchain in the model.

Medhane, et al. The advantage of the hybrid service framework is that it was able discover attacks against identification quickly, as shown in Fig. Blockchain-enabled cloud-edge-SDN integration security framework [ 83 ]. Lee, et al. The advantage of this paper is that the consensus mechanism used a RSA-based EPID scheme and a delay aware tree to evaluate the service delay. The author believed that they were the first work in blockchain technology to consider node mobility and network delay.

However, it only evaluated the performance service delay of the model with the random method. Data provenance records the history of a data object, which is essential for traceability, auditability, accountability, and privacy protection in cloud. However, the state-of-the-art research on data provenance is often too complex and lacks effectiveness. Based on the blockchain technique, X. Liang et al. ProvChain stores the provenance data in blockchain to make data operation transparent and traceable, thereafter establishing a trustworthy relationship among entities in cloud markets.

Figure 15 shows the system interaction model of ProvChain. In the proposed platform, regardless of the operation storing, sharing or obtaining data, it was recorded as a transaction in the blockchain network, and also the provenance data was stored in the provenance database.

System interaction model of ProvChain extended version of [ 85 ]. However, in the scheme, only the data of one single cloud provider could be authenticated, while the verification of cross-cloud, multi-clouds or federated clouds was not achieved, and the interoperability, data sharing and management in a multi-cloud environment was not handled well.

Currently, cloud computing is meeting the era of Internet of Everything IoE , where massive amounts of data are generated in cloud systems. How to ensure data reliability and traceability has become a significant challenge. To improve the security and accountability of IoT storage platform, R. Li et al. Edge devices were added to help IoT devices perform encryption and decryption operations. Blockchain plays a role in this paper as a third-party trust authentication authority.

Figure 16 shows the four-layered architecture of the model, in which numerous IoT sensors gather data to the edge devices, while edge writes the data into blockchain layer as transactions, and finally records it to the DHT network. The security of the model including protocol security, privacy, traceability and statistics are theoretically proven. Four-layered architecture for data management [ 86 ].

However, the ID-based access control in this scheme cannot be applied to a more complex authorization scenario, and only the theoretical model is provided. In order to protect the life cycle security of IoT data, H. Shafagh et al. The main contributions of the work are: it considered the life cycle security of IoT data, and it set up a grading policy to secure data management system, including three different levels.

However, it did not elaborate on how to implement the consensus and incentive mechanisms of blockchain, and the performance analysis was only theoretically carried out, without the experimental design. Yu et al. Through a case study of a wearable device, the feasibility of blockchain for trust-enabled IoT trading was made evident. In addition, this work gave a brief introduction on the challenges for future research in building a trustworthy trading platform for IoT ecosystems.

However, the framework of the blockchain-based IoT commodity was only theoretically defined and explained by the case study. Vehicle Edge Computing enhances the computing power of traditional VANETs, however with many new challenges, particularly serious security and trust risks. Based on blockchain technique, Yang et al. By utilizing the Bayesian Inference Model, vehicles were able to validate the messages received from their neighborhoods. In the proposed model, RSUs were responsible for evaluating the trust of the message to the related vehicles.

Two kinds of attack sources were considered in the model: attacks from malicious vehicles including message spoofing attacks, bad mouthing and ballot stuffing attacks, and attacks from compromised RSUs. Blockchain-based trust management model for vehicular networks [ 89 ]. The authors of the paper believed that an excellent trust model must incorporate the features of decentralization, tamper-proofing, consistency, timeliness, and availability, and they proved that blockchain technology was able to achieve these goals.

Data storage is an important type of cloud services. In view of the data application security, privacy leakage and trust crisis, as well as the performance bottleneck and single point of failure in the centralized data management center, researchers have proposed many distributed and blockchain-based schemes.

Li, et al. In the proposed architecture, the user files were divided into equal-length file blocks, and then encrypted, digitally signed and stored in the P2P network, shown in Fig. The blockchain-based transactions were also designed, including user renting cloud storage or renting their own free space.

The storage-related operations to each file block were recorded carefully in the body of each block in a safe, orderly and traceable manner. Blockchain-based distributed cloud data storage architecture [ 90 ].

It gave a comprehensive and detailed discussion on how to decide the number of copies in a distributed storage system. It used a generic algorithm to solve the problem of copy replacement between multiple users and multiple data centers, and it maintained the file loss rate and transmission delay in a very low level. In order to improve the security and effectiveness of data sharing, the fairness of data distribution, and protect the profits of data owner in a multi-cloud environment, Paper [ 91 ] proposed a novel architecture based on blockchain technology, as shown in Fig.

The architecture contained four parts: cloud users, the data service agent a third-party agency , the blockchain network, and data owners. The users sent data sharing request through the service agent, and obtained the corresponding data service after identity authentication and permission evaluation on blockchain. All data manipulation behaviors were recorded in the blockchain network. Data sharing architecture for cloud data sharing [ 91 ]. However, it is not a completely decentralized trust mdoel, since the deployment of the model still relied on a credible third party agency.

And other deletion strategies also have disadvantages such as non-verifiable deletion results, requires a trust third party for verification and low efficiency. To this end, C. Yang, et al. Figure 20 shows the basic steps in data storage. However, this method can only be used in the limited application of credible file deletion. Basic steps in data deletion [ 92 ]. The traditional file timestamp strategy requires a credible third-party service provider TSP , which may easily lead to reliability and single point of failure risks, along with the huge communication cost.

Zhu, et al. The uniqueness of this work is that it used both the ordinary voting nodes and the higher-level third-party trust authorities for transaction verification, which can be seen as a compromise strategy of blockchain and the traditional centralized architecture. The model improved the efficiency of consensus and dispute handling.

However, it is not a completely decentralized and self-management and evolutionary trust model. Kang et al. In addition, they developed a three-weight subjective logic- based reputation model to improve the credibility of data. The limitation of the model is that it does not explain how to determine the configuration of the related parameters, such as the setting of trust threshold, which has a great impact on whether the malicious vehicles can be effectively captured.

The summary of the comparison between the related works in the cloud data management is given in Tables 7 , 8 and 9. In this section, we concludes the comparison of the blockchain-based trust management approaches in cloud computing systems. These 35 blockchain-based trust management approaches are the research results of the last 3 years, showing that the blockchain-based scheme is very new and represents the latest trend in building decentralized and distributed trust.

From the perspective of country distribution, 18 came from China, 5 from the United States, 4 from Singapore, and the rest came from 8 different countries Australia, Germany, India, Argentina, Netherlands, Algeria, Switzerland and France , indicating that the scheme has been widely recognized by different research institutions of the world.

Academics from China, the United States, and Singapore focus more on this method. From the perspective of areas of interest, the researchers from China focus on blockchain-based basic trust frameworks, and blockchain-based cloud service applications including cloud storage and IoT applications , the US is more concerned with trusted data provenance and data storage applications, and researchers from Singapore pay attention to blockchain-based cloud resource allocation schemes. From the perspective of model performance argumentation, 7 of the 35 papers used theoretical argumentation and analysis methods, 20 used simulation experiments, 2 chose a prototype system, and 6 were on real testbed.

This indicates that blockchain-based trust management is still in the research stage and there is still a long way to the actual application. However, parts of the performance tests were done by theoretical argumentation or case study. By introducing blockchain technology, 22 of the 35 articles adopt a completely decentralized trust management model, 12 adopt a semi-decentralized model, and only 1 uses a centralized model, indicating that blockchain is sufficient to set up a decentralized trust framework and a non-tampering authentication model.

They applied blockchain-based trust management schemes in different environments, such as IoT, cloud computing, E-Commence, vehicular networks, etc. They involved different dimensions of trust, such as identity authentication, reputation management, data traceability, etc.

In addition, 12 of the 20 articles clearly analyzed the aimed attacks that could be dealt with in distributed decentralized architecture, including attacks on application scenarios and attacks against the chain structure. Obviously, the structure of a public blockchain is more suitable for building a point-to-point fully distributed, decentralized trust framework. However, a private blockchain or alliance blockchain has its own application field, such as in a closed or semi-closed system with a clear organizational structure, for example, IoT plus cloud hybrid computing environments.

Therefore, 17 of the 35 articles used public blockchain Bitcoin or Ethereum as the infrastructure for building trust relationships, 5 used Consortium blockchain, 2 used a private blockchain, 2 allowed both public and private blockchain, and 9 did not specify the type of blockchain they used. Unfortunately, when utilizing blockchain technology, none of the work provided a full discussion on how to deal with smart contracts, consensus mechanisms, and incentive mechanisms, possibly because few of them had been implemented in a real system, thus no detailed description on how to implement the related mechanisms.

In terms of consensus mechanisms, most papers still used traditional mechanisms, such as proof-of-work, proof-of-stake or a combination of the two, except a few works, for example in [ 67 , 70 , 75 , 81 , 95 ] which provided their own methods. This shows that the blockchain-based trust framework still has many open issues in relation to implementation and deployment and deserve further study and clarification.

Table 10 shows the summary of the comparisons. In order to utilize the massive computing and processing capabilities of a traditional cloud computing datacenter without losing the advantages of the end-to-end, decentralized, data preservation features of blockchain technology, this paper proposes a novel cloud-edge hybrid trust management framework, as shown in Fig.

The blockchain trust layer implements the peer-to-peer interconnection through the ubiquitous sensing components and communication protocols over the traditional IoT infrastructure layer and constructs distributed and decentralized trust management through blockchain architecture. Therefore, they can ensure lower latency and meet the needs of cross-group or cross-domain interactions.

The cloud trust layer is located at the top of the trust management framework. A complete trust authentication system includes identity authentication and behavior evaluation. In general, the identity information of a node is statically stable and relatively easy to authenticate and evaluate, even in a P2P network topology.

In contrast, trading behavior is dynamic, requiring a lot of computing power to record and evaluate. Therefore, to improve the integrity and efficiency of trust certification in real-time transactions, a cloud service transaction model based on double-blockchain structure is proposed, as shown in Fig. TAB is responsible for managing trust data in cloud service markets and provides trust evaluation results to other nodes.

Each block in TAB contains two parts: identity trust data and behavior trust data. When a node initially joins, only identity trust is added in a block, however, as time goes by and as transactions progress, its behavior trust is continuously written in a new block. Authentication is completed by a small number of supervisors, who can be normal miners or special nodes elected by the market authority.

Miners are responsible for storing and authenticating trust data and ensuring the consistency of the data through specifically designed consensus mechanisms. When nodes apply to enter the trading network, they must pay a fee to run a smart contract for the initial identity authentication. In addition, when they want to obtain the trust data of other nodes, they also pay a fee. This funding provides the incentive fee for the miners.

TTB is responsible for generating and storing the trading data block. In TBB, the miners have two tasks, one is to receive the latest transaction results and generate the transaction block, and the other is to evaluate behavior trust, generate a trust block, and then forward it to TAB. The corresponding trust block will be confirmed and stored by the miners in TAB.

In addition, double-chain mutual supervision provides a higher level of security and data traceability. At the same time, because the trust value is provided by the TAB, leaving the large-scale calculation or evaluation of trust on the TBB side, this can effectively reduce latency, and finally the application of blockchain can be realized in more real-time and high-reliability scenarios. Although many researchers have proposed strategies for the blockchain-based trust management, there are still huge gaps between theory and practical applications.

The future research directions are listed below and classified into four modules according to different trust research branches, as shown in Fig. Blockchain is a natural decentralized and P2P consensus framework. However, cloud computing systems have multiple construction modes, and with the emergence of fog computing, edge computing and IoT applications, the realization method of cloud has become more and more diversified.

In cloud computing systems, there exist many different kinds of trust relationship, including the cooperation and competition between provider and user, the cooperation and competition between broker and provider, the cooperation and competition between brokers or providers. Smart contracts, consensus mechanisms and incentive mechanisms are the critical issues in blockchain applications.

These issues still need to be addressed in the blockchain-based trust management. For example, how to encourage miners to actively participate in trust evaluation, trust decisions, and data verification, how to address the security issues of blockchain, such as attacks against smart contracts or blocks, forged transactions, etc. Blockchain is a kind of distributed ledger, which is very convenient to establish the complete and traceable transaction records between cloud entities.

However, some specific evaluation methods are required to compute trust from the original trading records. Therefore, it is necessary to explore an appropriate trust evaluation method and study how to generate trust block from trading history. On the other hand, blockchain is suitable for the behavioral trust management, but not good at the identity trust management. Because the identity trust authentication usually requires the assistance of a trusted third-party organization.

Therefore, to build decentralized trust authentication based on blockchain, it is necessary to solve the problem of how to complete identity trust management. Trust is conditionally transitive. People can estimate trust for an unfamiliar or never-before traded entity. In a blockchain-based trust network, it is easy to accurately obtain the trust degree of a node, however, how to calculate the recommendation trust and how to grant trust permissions to a composite application or other associated nodes, are still problems which need to be addressed.

Another issue is to improve the adaptability of blockchain-based trust management, realizing dynamic access control. A possible solution is to build a human-centric trust model, enabling services to intelligently assess their own security risks and apply a suitable security policy according to the potential attack. Identity trust is usually stable and relatively easy to handle, whereas behavior trust must be updated from time to time, incurring a huge trust management overhead.

The double-blockchain framework, proposed in the previous section, is one of the possible way to reduce the trust management overhead. However, the implementation details of the new framework still need to be solved. Therefore, researchers need to focus on appropriate measures to solve this problem for resource-constrained systems. Blockchain-based trust framework adopts a decentralized strategy, which requires each node in blockchain network has the ability of independent decision. Software agent represents the distributed AI technology.

Agents are independent, intelligent and social, and are very suitable for implementing self-maintenance and interactive behaviors on behalf of blockchain nodes. Agent-based trust decision enhances the intelligence of blockchain-based trust management.

At present, most of the research focuses on the fields of banking, electronic authentication, intelligent transportation, etc. However, in the future, blockchain-based schemes will penetrate into some other application scenarios. For example, in a cloud-edge hybrid architecture, when the basic infrastructure of trust is constructed by blockchain, it is important to redefine the relationship between cloud data center, edge server, and terminals, along with the judicious decision on how to realize an efficient and trust collaboration.

Privacy is another major issue which needs to be addressed. Data transparency and traceability are advantages brought by blockchain, however they also lead to privacy breaches and data abuse risks. Future research needs to find a balance between transparency and user privacy.

Trust is a simplified security mechanism. However, due to the degree of accuracy in trust evaluation, the existence of trust thresholds, and the possible attacks on trust itself, trust-enabled interaction still faces with some risks. Therefore, the balance mechanism between trust and risk needs to be further studied. At present, blockchain-based researches mostly use theoretical analysis or only simulation verification, lack the testing on a prototype or a real system.

Therefore, to develop a blockchain-based universal trust prototype system or platform is also a critical issue. This paper introduces a taxonomy and a review of blockchain-based trust management approaches in cloud computing systems. These approaches are classified into different taxonomies according to three phases: blockchain-based basic trust framework, blockchain-enhanced trust interaction framework and mechanisms, and data management.

Then, it presents a comprehensive analysis and comparison of the existing blockchain-based trust approaches. In order to improve the efficiency and adaptiveness of trust-enabled cloud computing, a novel cloud-edge hybrid trust management framework along with a double-blockchain based cloud transaction model are proposed. Finally, we suggest future directions and detail the open challenges of blockchain-based trust management schemes. The uniqueness of this paper is that it studies the application of blockchain from the perspective of trust.

Our analysis shows that using blockchain technology to construct a decentralized trust management framework has the following benefits:. However, the article also reveals that there is a huge gap between the theory of the method and the actual application. All in all, utilizing the blockchain technique to build a more credible and safe cloud transaction environment is a promising research direction.

ACM Comput Surve 52 1 :1— Article Google Scholar. Li X, Gui X Cognitive model of dynamic trust forecasting. J Software 21 1 — Appl Soft Comput 34 — Procedia Computer Sci 54 — IEEE, pp — Google Scholar. Springer, Berlin, pp 69— Cluster Comput 17 1 — Int J Multimedia Ubiquitous Engineering — IEEE Access — IEEE Access 8 — IEEE —8. Harbajanka S, Saxena P Survey paper on trust management and security issues in cloud computing. Symposium on colossal data analysis and networking CDAN.

IEEE —3. Huang J, Nicol D Trust mechanisms for cloud computing. ACM Comput Survey 46 1 :1— IEEE — Matin C, Navimipour J, Jafari N Cloud computing and trust evaluation: a systematic literature review of the state-of-the-art mechanisms. J Electrical Syst Information Technol 5 3 — J Service Sci Res 9 1 :1— Deshpande S, Ingle R Trust assessment in cloud environment: taxonomy and analysis. In: Proceedings of international conference on computing. IEEE , pp — Hawlitschek F, Notheisen B, Teubner T The limits of trust-free systems: a literature review on blockchain technology and trust in the sharing economy.

Electron Commer Res Appl 29 — ACM Computing Surveys 48 2 :1— ACM Comput Surveys 48 2 :1— Saad M, et al. Springer, Boston. Li H Study on trust model and controversy discovery under web 2. Doctor thesis, XiDian University, China. Commun ACM 43 12 — ACM — In proceedings of international conference on performance evaluation and modeling in wired and wireless networks PEMWN. Abdallah, M. Zulkernine, Y. Gu , et al. IEEE Singh S, Sidhu J A collaborative trust calculation scheme for cloud computing systems.

Nagarajan R, Selvamuthukumaran S, Thirunavukarasu R A fuzzy logic based trust evaluation model for the selection of cloud services. In proceedings of IEEE international conference on networking. Yefeng R, Durresi A A trust management framework for cloud computing platforms. Felipe B, Fiorese A A trust reputation architecture for cloud computing environment. Kashif, Z. Memon, A. Balouch, et al. Distributed trust protocol for IaaS cloud computing.

J Commun 7 — Journal of Xidian University 4 — Yan S, Zheng X A user-centric trust and reputation method for service selection. In proceedings of the international symposium on intelligence information processing and trusted computing. Du, J. Tian, H. Cloud service selection model based on trust and personality preferences. In proceedings of 15th annual conference on privacy. In proceedings of IEEE 11th international conference on cloud computing.

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Lei, et al. Inf Sci — Yang C, Chen et al Blockchain-based publicly verifiable data deletion scheme for cloud storage. J Network Comput Application — Kang, R. Yu, X. Huang, et al. Blockchain for secure and efficient data sharing in vehicular edge computing and networks, IEEE internet of things journal, early access.

Download references. The authors would like to thank the support of the laboratory, university and government, and the hard work of the editors and reviewers. You can also search for this author in PubMed Google Scholar. All authors take part in the discussion of the work described in this paper. The author s read and approved the final manuscript. Wenjuan Li received a Ph. D degree in from Zhejiang University, Hangzhou China, in computer science.

Her research interests include cloud computing, social network and trust. Jiyi Wu received a Ph. His research interests include service computing, trust and reputation. His research interests include networks computing, service computing, and data analytics.

He has authored or co-authored over journal and conference papers in the above-mentioned areas. Nan Chen is a lecturer in Hangzhou Normal University. Her research interests include social network and trust management. Qifei Zhang received a Ph. He is currently an associate researcher in Zhejiang University. His research interests include IoT, cloud computing and P2P network. He is also serving as the founding CEO of Manjrasoft, a spin-off company of the University, commercializing its innovations in Cloud Computing.

For further information on Dr. Buyya, please visit his cyberhome: www. Correspondence to Wenjuan Li. We formally declare that there are no know financial or non-financial competing interests in the realization of this research. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Reprints and Permissions. Li, W. Blockchain-based trust management in cloud computing systems: a taxonomy, review and future directions. J Cloud Comp 10, 35 Download citation. Received : 21 January Accepted : 20 May Published : 21 June Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative.

Skip to main content. Search all SpringerOpen articles Search. Download PDF. Abstract Through virtualization and resource integration, cloud computing has expanded its service area and offers a better user experience than the traditional platforms, along with its business operation model bringing huge economic and social benefits.

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