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Zhang, Nan; Yao, Li; Nenadić, Aleksandra; Chin, Jay; Goble, Carole; Rector, Alan L.; Chadwick, David; Otenko, Sassa; Shi, Qi

doi:10.1002/cpe.v19:9

Cited by 4 publications

(2 citation statements)

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“…The first and the only effort so far (to the authors best knowledge) on linking authentication LoA to authorisation decision making at run-time was made by the FAME-PERMIS (Flexible Authentication Middleware Extensions to the PERMIS) project team [www.fame-permis.org]. The project developed a software component that derives a LoA value based upon a user's authentication token presented to the authentication service, and asserts the value to a role-based access control decision engine run at the SP (Service Provider) side thus achieving LoA lined access control [9,15]. However, the software is in a very basic form; it only implements the LoA definition versus token types as defined by the NIST guideline [2].…”

Section: Related Workmentioning

confidence: 99%

“…Optionally, these data may be stored in a third party attribute directory or an attribute authority for consumptions by other relying parties. For example, the data may be sent to the attribute authority in the Shibboleth system for attribute assertion [14,15], or to the attribute authority for creating and assigning an attribute certificate.…”

Section: Authentication Loa Derivation Module (Aloa-dm)mentioning

confidence: 99%

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A Generic Authentication LoA Derivation Model

Yao

Zhang

2009

Emerging Challenges for Security, Privacy and Trust

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One way of achieving a more fine-grained access control is to link an authentication level of assurance (LoA) derived from a requester's authentication instance to the authorisation decision made to the requester. To realise this vision, there is a need for designing a LoA derivation model that supports the use and quantification of multiple LoA-effecting attributes, and analyse their composite effect on a given authentication instance. This paper reports the design of such a model, namely a generic LoA derivation model (GEA-LoADM). GEA-LoADM takes into account of multiple authentication attributes along with their relationships, abstracts the composite effect by the multiple attributes into a generic value, authentication LoA, and provides algorithms for the run-time derivation of LoA. The algorithms are tailored to reflect the relationships among the attributes involved in an authentication instance. The model has a number of valuable properties, including flexibility and extensibility; it can be applied to different application contexts and support easy addition of new attributes and removal of obsolete ones.

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Section: Related Workmentioning

confidence: 99%

Section: Authentication Loa Derivation Module (Aloa-dm)mentioning

confidence: 99%

A Generic Authentication LoA Derivation Model

Yao

Zhang

2009

Emerging Challenges for Security, Privacy and Trust

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Design and implementation of algorithm execution time predictor for remote sensing product workflow

Zhou,

Wu,

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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Remote sensing product production is an important task in the field of remote sensing engineering. Compared with the production process of remote sensing products, the implementation process of remote sensing product production is a multi-task serial and highly concurrent process that integrates a large number of remote sensing digital image processing algorithms and various processes. Remote sensing product workflows transform complex computing tasks into processes that can be described, managed, and automated. In the workflow, algorithms are the smallest execution unit of tasks, and their execution time has a significant impact on the performance and efficiency of the entire workflow. Therefore, accurate prediction of algorithm execution time is crucial for optimizing workflow performance. This paper proposes and designs an algorithm execution time predictor for remote sensing product workflows. Firstly, feature extraction algorithms are used to extract features from algorithm execution logs, and the prediction model suitable for this scenario is determined by analyzing the characteristics of operators in remote sensing product workflows. Then, the model is trained using historical data sets, and the test set results are compared with the actual execution time to verify the accuracy and practicality of the model. Experimental results show that the proposed algorithm execution time predictor outperforms the baseline model in terms of prediction accuracy and time efficiency.

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A computational field framework for collaborative task execution in volunteer clouds

Sebastio

Amoretti

Lafuente

2014

Proceedings of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

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The increasing diffusion of cloud technologies offers new opportunities for distributed and collaborative computing. Volunteer clouds are a prominent example, where participants join and leave the platform and collaborate by sharing computational resources. The high complexity, dynamism and unpredictability of such scenarios call for decentralized self-* approaches. We present in this paper a framework for the design and evaluation of self-adaptive collaborative task execution strategies in volunteer clouds. As a byproduct, we propose a novel strategy based on the Ant Colony Optimization paradigm, that we validate through simulation-based statistical analysis over Google workload data.

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Cited by 4 publications

References 0 publications

A Generic Authentication LoA Derivation Model

A Generic Authentication LoA Derivation Model

Design and implementation of algorithm execution time predictor for remote sensing product workflow

A computational field framework for collaborative task execution in volunteer clouds

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