CROSA: Context-aware cloud service ranking approach using online reviews based on sentiment analysis

[EN] The explosion of cloud services over the Internet has raised new challenges in cloud service selection and ranking. The existence of a great variety of offered cloud services made the users think deeply about the most appropriate services that meet their needs and at the same time are adaptable to their context. Nowadays, online reviews are used for the purpose of enhancing the effectiveness of finding useful product information, having impact on the consumers' decision-making process. In this context, the current paper suggests a context-aware cloud service ranking approach using online reviews and based on sentiment analysis (CROSA). Its main objective is to ease the cloud service selection. The CROSA approach analyzes sentiments associated with service measurement index (SMI)-based service properties for each alternative cloud service. Moreover, it enhances the cloud service decision-making by supporting fuzzy sentiments through the intuitionistic fuzzy set theory and PROMETHEE II. The experimental results presented in this paper show that this approach is efficient and performing.Ben-Abdallah, E.; Boukadi, K.; Lloret, J.; Hammami, M. (2021). CROSA: Context-aware cloud service ranking approach using online reviews based on sentiment analysis. Concurrency and Computation: Practice and Experience. 33(7):1-16. https://doi.org/10.1002/cpe.5358S11633

Analysing Trust Issues in Cloud Identity Environments

Trust acts as a facilitator for decision making in environments, where decisions are subject to risk and uncertainty. Security is one of the factors contributing to the trust model that is a requirement for service users. In this paper we ask, What can be done to improve end user trust in choosing a cloud identity provider? Security and privacy are central issues in a cloud identity environment and it is the end user who determines the amount of trust they have in any identity system. This paper is an in-depth literature survey that evaluates identity service delivery in a cloud environment from the perspective of the service user

Trustworthy Federated Learning: A Survey

Federated Learning (FL) has emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL increases, addressing trustworthiness issues in its various aspects becomes crucial. In this survey, we provide an extensive overview of the current state of Trustworthy FL, exploring existing solutions and well-defined pillars relevant to Trustworthy . Despite the growth in literature on trustworthy centralized Machine Learning (ML)/Deep Learning (DL), further efforts are necessary to identify trustworthiness pillars and evaluation metrics specific to FL models, as well as to develop solutions for computing trustworthiness levels. We propose a taxonomy that encompasses three main pillars: Interpretability, Fairness, and Security & Privacy. Each pillar represents a dimension of trust, further broken down into different notions. Our survey covers trustworthiness challenges at every level in FL settings. We present a comprehensive architecture of Trustworthy FL, addressing the fundamental principles underlying the concept, and offer an in-depth analysis of trust assessment mechanisms. In conclusion, we identify key research challenges related to every aspect of Trustworthy FL and suggest future research directions. This comprehensive survey serves as a valuable resource for researchers and practitioners working on the development and implementation of Trustworthy FL systems, contributing to a more secure and reliable AI landscape.Comment: 45 Pages, 8 Figures, 9 Table

Contribución a la estimulación del uso de soluciones Cloud Computing: Diseño de un intermediador de servicios Cloud para fomentar el uso de ecosistemas distribuidos digitales confiables, interoperables y de acuerdo a la legalidad. Aplicación en entornos multi-cloud.

184 p.El objetivo del trabajo de investigación presentado en esta tesis es facilitar a los desarrolladores y operadores de aplicaciones desplegadas en múltiples Nubes el descubrimiento y la gestión de los diferentes servicios de Computación, soportando su reutilización y combinación, para generar una red de servicios interoperables, que cumplen con las leyes y cuyos acuerdos de nivel de servicio pueden ser evaluados de manera continua. Una de las contribuciones de esta tesis es el diseño y desarrollo de un bróker de servicios de Computación llamado ACSmI (Advanced Cloud Services meta-Intermediator). ACSmI permite evaluar el cumplimiento de los acuerdos de nivel de servicio incluyendo la legislación. ACSmI también proporciona una capa de abstracción intermedia para los servicios de Computación donde los desarrolladores pueden acceder fácilmente a un catálogo de servicios acreditados y compatibles con los requisitos no funcionales establecidos.Además, este trabajo de investigación propone la caracterización de las aplicaciones nativas multiNube y el concepto de "DevOps extendido" especialmente pensado para este tipo de aplicaciones. El concepto "DevOps extendido" pretende resolver algunos de los problemas actuales del diseño, desarrollo, implementación y adaptación de aplicaciones multiNube, proporcionando un enfoque DevOps novedoso y extendido para la adaptación de las prácticas actuales de DevOps al paradigma multiNube

A Trust Management Framework for Decision Support Systems

In the era of information explosion, it is critical to develop a framework which can extract useful information and help people to make “educated” decisions. In our lives, whether we are aware of it, trust has turned out to be very helpful for us to make decisions. At the same time, cognitive trust, especially in large systems, such as Facebook, Twitter, and so on, needs support from computer systems. Therefore, we need a framework that can effectively, but also intuitively, let people express their trust, and enable the system to automatically and securely summarize the massive amounts of trust information, so that a user of the system can make “educated” decisions, or at least not blind decisions. Inspired by the similarities between human trust and physical measurements, this dissertation proposes a measurement theory based trust management framework. It consists of three phases: trust modeling, trust inference, and decision making. Instead of proposing specific trust inference formulas, this dissertation proposes a fundamental framework which is flexible and can be adapted by many different inference formulas. Validation experiments are done on two data sets: the Epinions.com data set and the Twitter data set. This dissertation also adapts the measurement theory based trust management framework for two decision support applications. In the first application, the real stock market data is used as ground truth for the measurement theory based trust management framework. Basically, the correlation between the sentiment expressed on Twitter and stock market data is measured. Compared with existing works which do not differentiate tweets’ authors, this dissertation analyzes trust among stock investors on Twitter and uses the trust network to differentiate tweets’ authors. The results show that by using the measurement theory based trust framework, Twitter sentiment valence is able to reflect abnormal stock returns better than treating all the authors as equally important or weighting them by their number of followers. In the second application, the measurement theory based trust management framework is used to help to detect and prevent from being attacked in cloud computing scenarios. In this application, each single flow is treated as a measurement. The simulation results show that the measurement theory based trust management framework is able to provide guidance for cloud administrators and customers to make decisions, e.g. migrating tasks from suspect nodes to trustworthy nodes, dynamically allocating resources according to trust information, and managing the trade-off between the degree of redundancy and the cost of resources

Trust beyond reputation: A computational trust model based on stereotypes

Models of computational trust support users in taking decisions. They are commonly used to guide users' judgements in online auction sites; or to determine quality of contributions in Web 2.0 sites. However, most existing systems require historical information about the past behavior of the specific agent being judged. In contrast, in real life, to anticipate and to predict a stranger's actions in absence of the knowledge of such behavioral history, we often use our "instinct"- essentially stereotypes developed from our past interactions with other "similar" persons. In this paper, we propose StereoTrust, a computational trust model inspired by stereotypes as used in real-life. A stereotype contains certain features of agents and an expected outcome of the transaction. When facing a stranger, an agent derives its trust by aggregating stereotypes matching the stranger's profile. Since stereotypes are formed locally, recommendations stem from the trustor's own personal experiences and perspective. Historical behavioral information, when available, can be used to refine the analysis. According to our experiments using Epinions.com dataset, StereoTrust compares favorably with existing trust models that use different kinds of information and more complete historical information