QoS-based Trust Evaluation for Data Services as a Black Box

Romdhani, Senda; Vargas-Solar, Genoveva; Bennani, Nadia; Ghedira-Guegan, Chirine

doi:10.1109/icws53863.2021.00067

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…TMSs implement a trust negotiation protocol to establish trust between pairs of services by matching the trust requirements of a service with the service profile of another. Trust requirements model the functional and non-functional behavior that another service must demonstrate to establish trust [10], [18]. The service profile includes information used by a service to establish trust with another service, such as data on its functional and nonfunctional behavior (e.g., response time [1], QoS [18]), reputation-based information (e.g., [8], [13]), possibly referred to the entire application (e.g., [1]).…”

Section: Trust Management In Data Economy: Limitations and Motivationsmentioning

confidence: 99%

“…Trust requirements model the functional and non-functional behavior that another service must demonstrate to establish trust [10], [18]. The service profile includes information used by a service to establish trust with another service, such as data on its functional and nonfunctional behavior (e.g., response time [1], QoS [18]), reputation-based information (e.g., [8], [13]), possibly referred to the entire application (e.g., [1]). The out-THEME come of the protocol is a binary result (success or failure) retrieved by matching requirements with service profiles, specifying whether the trust is established or not [1].…”

Section: Trust Management In Data Economy: Limitations and Motivationsmentioning

confidence: 99%

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Revisiting Trust Management in the Data Economy: A Road Map

Ardagnaa,

Bena,

Bennani

et al. 2024

IEEE Internet Comput.

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In the last two decades, multiple ICT evolutions boosted the ability to collect and analyze vast amounts of data (on the order of Zettabytes). Collectively, they paved the way for the so-called data economy, revolutionizing most sectors of our society, including healthcare, transportation, and grids. At the core of this revolution, distributed data-intensive applications compose services operated by multiple parties in the cloud-edge continuum; they process, manage and exchange massive amounts of data at an unprecedented rate. However, data hold little value without adequate data protection. Traditional solutions, which aim to balance data quality and protection, are insufficient to address the peculiarities of the data economy, including trustworthy data sharing and management, composite service support, and multi-party data life cycle. This article analyzes how trust management systems can regain the lead in supporting trustworthy data-intensive applications, discussing current challenges and proposing a roadmap for new-generation trust management systems in the data economy.The data economy is commonly defined as an ecosystem of players collaborating to share digital data as products and services, to create applications that extract value from them. 1 The data economy transforms the design and development of applications, prioritizing data processing, management and sharing. This shift is fostering the emergence of new platforms and environments, such as data marketplaces and data spaces, where data can be pooled and shared to maximize data quality and trustworthiness, and distributed data management systems ensuring guarantees to store, manage versions, and supply data XXXX-XXX © 2024 IEEE Digital Object Identifier 10.1109/XXX.0000.00000001 European Commission, "Communication on Building a European Data Economy", https://ec.europa.eu/newsroom/dae/ document.cfm?doc_id=41205 for complex analytics processes. Data marketplaces 2 are (cloud) platforms where users can engage in selfservice buying and selling of data, ensuring both security and high quality. Data spaces expand upon this concept to define a complete environment where data infrastructures and governance frameworks are integrated to streamline data management. Within these environments, data consumers and providers collaborate to surmount existing legal and technical barriers to data sharing, thereby securely unlocking the full potential of their data. 3 The European Union (EU) is leading the way in this 2 https://joinup.ec.europa.eu/collection/elise-europeanlocation-interoperability-solutions-e-government/glossary/ term/data-marketplace 3 https://internationaldataspaces.org/, https://digitalstrategy.ec.europa.eu/en/library/staff-working-documentdata-spaces Month

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Section: Trust Management In Data Economy: Limitations and Motivationsmentioning

confidence: 99%

Section: Trust Management In Data Economy: Limitations and Motivationsmentioning

confidence: 99%

Revisiting Trust Management in the Data Economy: A Road Map

Ardagnaa,

Bena,

Bennani

et al. 2024

IEEE Internet Comput.

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Towards Practical Dynamic Trust Monitoring of Containerized Services in NFV Infrastructure

De Riggi,

Vogt,

Kalinagac

et al. 2023

ICC 2023 - IEEE International Conference on Communications

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Although Network Function Virtualization (NFV) and containerized services embedded therein are an already active research field while becoming increasingly widespread in practice (e.g., SG networks), the trust and security challenges still deserve more attention. To tackle relevant issues for this aspect, our work deals with the question of whether and how the issue of trust assessment can be addressed in such infrastructures. Different trust models are reviewed, and the trust attributes used in the literature are analysed and evaluated. These parameters are subsequently included in a trust calculation framework for their confidence analysis. Finally, a Dynamic Trust Monitoring (DTM) solution, namely MicroDTM, that supervises the trustworthiness of containerized services in an NFV infrastructure is proposed. By collecting and processing the trust parameters, a containerized service environment is evaluated according to trustworthiness for different scenarios. In addition to performance analysis, improvements and extensions necessary to use the system in a practical environment are identified.1 MicroDTM source code is available at https://github.com/BA22gueu01/ MicroDTM as an open-source project.

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A Comprehensive and Unified Approach to Web Service Trust Evaluation Based on Uncertainty Methodology

Zhang

2022

Entropy

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Web services have the advantage of being able to generate new value-added services based on existing services. To effectively compose Web services, the composition process necessitates that the services that will participate in a given composite service are more trustworthy than those that provide similar functionality. The trust mechanism appears to be a promising way for determining service selection and composition. Existing trust evaluation approaches do not take into account customer expectations. Based on fuzzy set theory and probability theory, this work proposes a unique Web service trust evaluation approach that is notable for its ability to provide personalized service selection based on customer expectations and preferences. The proposed approach defines trust as a fuzzy notion that is related to prior experiences and ratings, and expresses trust in two different forms. This work mainly solves two key issues in Web service trust architectures, bootstrapping trust for the newcomer services and deriving trust for composite services. The proposed approach combines the solutions to numerous issues in a natural way. The case study and approaches comparison demonstrate that the proposed approach is feasible.

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QoS-based Trust Evaluation for Data Services as a Black Box

Cited by 3 publications

References 18 publications

Revisiting Trust Management in the Data Economy: A Road Map

Revisiting Trust Management in the Data Economy: A Road Map

Towards Practical Dynamic Trust Monitoring of Containerized Services in NFV Infrastructure

A Comprehensive and Unified Approach to Web Service Trust Evaluation Based on Uncertainty Methodology

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