Trustworthy Service Providers Research Articles

Study on trust evaluation and service selection for Service-Oriented E-Commerce systems in IoT environments

Trust-based service management in IoT systems, a promising research direction provides an intelligent solution for the identification of appropriate and trustworthy service providers from a huge of number smart nodes providing similar services. An efficient trust-based service ranking scheme (TBSRS) is proposed. The novelty of our design lies in providing accurate trust evaluation and service ranking in the absence of assessment data or presence of untrustworthiness recommendations. TBSRS also addresses the issue about the trustworthiness evaluation of a new service. Service providers are ranking based on the binary fruit fly optimization algorithm in TBSRS. The experimental results demonstrate desirable convergence, accuracy, and resiliency properties of TBSRS, and also demonstrate that TBSRS outperforms Adaptive IoT Trust and IoT-HiTrust in terms of the precision of service selection and other a few performance metrics.

Enhancing system robustness of active products using trust based collaboration

A robust collaborative system of active products (a product is called active when its ownership does not get transferred from provider to requestor at the time of its usage) should have an in-built mechanism which can make entities (service provider(s) and requestor(s)) to decide with whom to collaborate. In the absence of such a mechanism, the system is bound to have high job failure rate, resulting in wastage of resources. This paper proposes a Trust based Multi-Agent Framework (TbMAF) for collaborative systems of active products which enable only trustworthy entities to collaborate, safeguarding both users’ sensitive applications and providers’ resources. The trustworthiness of service provider(s) and requestor(s) is computed using Fuzzy Inference System (FIS) and Radial Basis Function Neural Network (RBFNN) methodologies, respectively. A prototype based on the proposed system has been tested using real time data of a collaborative system namely, EGEE (Enabling Grids for E-science). This paper finds evidence that the job failure rate is lower when collaborations take place only between trustworthy entities. Further, the proposed framework is found to be robust against malicious entities and can capture the evolving behavior of entities as well.

Robust, Secure, and Adaptive Trust-Oriented Service Selection in IoT-Based Smart Buildings

Internet of Things (IoT) has become an integral part of a smart community that connects computing devices, smart objects, and mechanical and digital machines, having unique identifiers, to communicate with each, without human intervention. This smart connectivity within a building assists the users in real time to provide an enormous range of connected applications and facilitate the optimized use of multiple resources. These smart building applications can make our lives more comfortable and provide a more sustainable, healthy, and safe workplace. A key challenge for IoT toward smart buildings is to ensure that the service has been taken from a trustworthy service provider. It requires a reliable, robust, and adaptive context-oriented trust mechanism that conforms to the specific requirements of the end users. To enhance the security of smart buildings in IoT, this research proposes an adaptive context-based trust evaluation system for smart building (CTES-SB) applications. The trust score for service is calculated based on the client’s previous interaction and recommendation from context-similar clients. Using CTES-SB, the client selects the best service provider based on the previous and current trust scores for the next interaction. The model also helps to filter out malicious nodes through an indirect trust calculation process. This process dynamically assigns weights based on direct interactions and trustworthy recommendations for detecting and avoiding malicious interactions. We have demonstrated the effectiveness of CTES-SB by simulating multiple smart building scenarios under malicious attacks. The proposed architecture of CTES-SB has been experimentally evaluated to benchmark its performance for best service selection and resiliency against malicious nodes. The CTES-SB is proved to be efficient by having a comparison with the state-of-the-art algorithms. The comparison is in terms of filtering the malicious nodes from the network and the result shows that the trust converges quickly toward the ground-truth value.

TGSM: Towards trustworthy group-based service management for social IoT

The Social Internet of Things has been introduced as a social oriented approach to deal with IoT challenges. A service discovery process is also transformed by this paradigm. Since the Social Internet of Things includes millions of services across the network, the question arises that to what extent can we trust service providers? The appropriate selection of trustworthy service provider plays a key role in successful deployment of the SIoT. In this work, we address this issue by proposing a trustworthy group-based service management model (TGSM). The proposed discovery and selection process of an appropriate service provider relies on both trustworthiness measurement and performance assessment. In the trustworthiness measurement case, a punishment mechanism is adopted in trustworthiness evaluation to deal with objects’ selfish behavior. Likewise, we employed the HITS algorithm to propose a global dynamic reputation evaluation approach. We propose a service-based grouping approach as a novel architecture for SIoT network. We also consider the dynamic evolving of social relationships among the SIoT devices. Our SIoT paradigm, due to its intrinsically multilayered nature, is modeled upon a multiplex network. In the performance measurement case, some metrics are used to asses objects’ performance. To evaluate the proposed trust model, we extend the CRAWDAD dataset, and the objects in the dataset are categorized into five types. The simulation results show that the proportions of four selfish types of objects are dominated by completely trustworthy objects, which proves the effectiveness of our proposed TGSM model. Moreover, the results demonstrate HITS centrality measurement’s effectiveness, which leads the proportion of trustworthy service providers to nearly double in comparison with degree and eigenvector centrality in this context.

Trust management of services (TMoS): Investigating the current mechanisms

AbstractGenerally, service interactions in dynamic environments comprising cloud computing, Internet of Things (IoT), and etc. occur in an unspecified situation causing trust management a significant aspect. The widespread usage of services in today's vastly high‐tech world reveals the rising necessity of trust management in terms of services. In dynamic environments, there are not enough confidences for users to recognize trustworthy service providers. Hence, evaluating and managing the trustworthiness of services is a vital challenge for enabling customers to select trustworthy resources in dynamic environments. To the extent of our knowledge, in spite of the vital role of Trust Management of Services (TMoS), there is not any thorough and systematic work in this scope with a specific focus on services. Thus, this research investigates the current methods of trust management in terms of services, which are published up to February 2020. We have identified 68 papers that are diminished to 22 mostly qualified papers through the article selection process. Also, we have examined and compared the selected papers in terms of their merits and demerits, considering the important parameters in this field. The investigation results point out that the security, scalability, and dynamicity are essential to almost all research. Furthermore, privacy and reliability are the most essential parameters in the field of TMoS. Moreover, the statistical results and information can contribute to future works; also, we have conducted some open issues and future work suggestions that could be an effective roadmap for future researchers in this area.

A Matrix Factorization Model for Hellinger-Based Trust Management in Social Internet of Things

The Social Internet of Things (SIoT), integration of the Internet of Things and Social Networks paradigms, has been introduced to build a network of smart nodes that are capable of establishing social links. In order to deal with misbehaving service provider nodes, service requestor nodes must evaluate their trustworthiness levels. In this paper, we propose a novel trust management mechanism in the SIoT to predict the most reliable service providers for each service requestor, which leads to reduce the risk of being exposed to malicious nodes. We model the SIoT with a flexible bipartite graph (containing two sets of nodes: service providers and service requestors), then build a social network among the service requestor nodes, using the Hellinger distance. Afterward, we develop a social trust model using nodes' centrality and similarity measures to extract trust behaviors among the social network nodes. Finally, a matrix factorization technique is designed to extract latent features of SIoT nodes, find trustworthy nodes, and mitigate the data sparsity and cold start problems. We analyze the effect of parameters in the proposed trust prediction mechanism on prediction accuracy. The results indicate that feedbacks from the neighboring nodes of a specific service requestor with high Hellinger similarity in our mechanism outperforms the best existing methods. We also show that utilizing the social trust model, which only considers a similarity measure, significantly improves the accuracy of the prediction mechanism. Furthermore, we evaluate the effectiveness of the proposed trust management system through a real-world SIoT use case. Our results demonstrate that the proposed mechanism is resilient to different types of network attacks, and it can accurately find the most proper and trustworthy service provider.

An improved rough set approach for optimal trust measure parameter selection in cloud environments

The existence of a multitude of cloud service providers (CSPs) for each service type increases the difficulty in the identification of appropriate and trustworthy service providers based on their abilities and cloud users’ unique functional and non-functional quality-of-service (QoS) requirements. Further, the dynamic nature of the cloud ecosystem in terms of performance and new services increases the complexity of the cloud service selection problem. Trust-based service selection mechanisms which involve the intrinsic relations among the QoS parameters or trust measure parameters (TMPs) to evaluate the quality of the CSPs are the most preferred solution for the problem of cloud service selection. However, the accuracy of the trust-based service selection models and the CSP’s trust value relies on the optimality of the TMP subset obtained with respect to the service type. Hence, this work presents an efficient rough set theory-based hypergraph-binary fruit fly optimization (RST-HGBFFO), a cooperative bio-inspired technique to identify the optimal service-specific TMPs. Experiments on QWS dataset, Cloud Armor, and CISH—SASTRA trust feedback dataset reveal the predominance of RST-HGBFFO over the state-of-the-art feature selection techniques. The performance of RST-HGBFFO feature selection technique was validated using hypergraph-based computational model and WEKA tool in terms of reduct size, service ranking, classification accuracy, and time complexity.

Social context-aware trust paths finding for trustworthy service provider selection in social media

Online Social Network (OSN) has been used to enhance service provision and service selection, where trust is one of the most important factors for the decision making of service consumers. Thus, a significant and challenging problem is how to effectively and efficiently find those social trust paths that can yield trustworthy trust evaluation results based on the requirements of a service consumer particularly in contextual OSNs which contains social contexts, like social relationships and social trust between participants, and social positions of participants. In this paper, we propose a new concept called Strong Social Graph (SSG), consisting of participants with strong social connections. We also propose an approach to identify SSGs, and propose a novel index method and a graph compression method for SSG. Then based on the compressed SSG and indices, we propose a new efficient and effective approximation algorithm, called SSG-MCBA by adopting the Monte Carlo method and our optimization search strategies. The experiments conducted onto two real social network datasets illustrate that SSG-MCBA greatly outperforms the state-of-the-art method in both efficiency and effectiveness.

Dynamic ranking of cloud services for web based cloud communities: Efficient algorithm for rating based discovery and multi-level ranking of cloud services

Trust assessment in cloud depends on QoS attributes. While evaluating the trustworthiness of the CSPs, traditional methods of trust assessment do not take into account QoS attributes that keep changing dynamically. To address the issue of determining the trustworthiness of CSPs in web, rating based dynamic discovery (RBDD) of QoS attributes that keeps changing periodically and multi-layer ranking (MLR) algorithm that ranks the discovered CSPs in an efficient manner have been proposed. This approach allows us to evaluate CSPs trustworthiness from cloud auditors perception. evaluation results indicates that the RBDD is capable of sensing behavioural changes in CSPs and discovers the dynamic trustworthy service providers and MLR algorithm ranks them based on CC requirements with high accuracy and minimal time complexity compared to other approaches. The proposed system has been validated with synthetic dataset owing to absence of standardisation.

A trust centric optimal service ranking approach for cloud service selection

Cloud service selection, a promising research directive provides an intelligent solution via. service ranking based on the Quality of Service (QoS) attributes for the identification of trustworthy Cloud Service Providers (CSPs) among a wide range of functionally-equivalent CSPs. Further, the impact of objective and subjective assessment data on the accuracy of the service selection model makes the credibility of the assessment data, a major concern for the researchers in service-oriented environments. To address the challenges with respect to the identification of the user requirement compliant CSPs, data credibility, service ranking, etc. we present Hypergraph –Binary Fruit Fly Optimization based service ranking Algorithm (HBFFOA), a trust-centric approach for the identification of suitable and trustworthy cloud service providers. HBFFOA employs hypergraph partitioning, time-varying mapping function, helly property, and binary fruit fly optimization algorithm for the identification of similar service providers, credibility based trust assessment, selection of trustworthy service providers, and optimal service ranking respectively. Experiments using synthetic QoS dataset from WSDream#2 illustrates the effectiveness, practicability, scalability and computational attractiveness of HBFFOA over the existing service selection approaches in terms of precision, stability, statistical test, and time complexity analysis.

Multifaceted trust management framework based on a trust level agreement in a collaborative cloud

Cloud computing is an emerging field aimed at providing any type of service on demand -anywhere, and anytime. Advances in this field have facilitated a futuristic collaborative cloud in which many inter-connected clouds provide scalable services. A request processed by an untrustworthy provider can endanger quality of service, prompting the dissatisfaction of the customer. This paper aims to select the Trustworthy Service Provider (TSP) by evaluating trust based on in-context feedback from different sources; these sources include customer feedback, global advisory feedback and third-party feedback. Furthermore, unfair feedback is filtered to improve accuracy. Consequently, the job-success rate increases, and customers are able to obtain value-for-money for the sum paid toward selection of a provider. The present experimental work proves that the proposed model is effective and efficient in selecting a trustworthy provider in a collaborative environment.

Improved TOPSIS Method Based Trust Evaluation Framework for Determining Trustworthiness of Cloud Service Providers

Trust is one of the most challenging issues in the emerging cloud computing era. Over the past few years, numerous cloud service providers have been emerged providing similar kinds of services. It has become incredibly complex for the cloud clients to make a distinction among multiple cloud service providers offering similar kinds of services. Cloud clients need trustworthy service providers who provide services exactly as per the SLA and do not deviate from their promises. Though, there have been significant efforts to form trust between service providers and the clients by providing data, storage and network security, but relatively fewer efforts have been made in the field of trustworthiness determination by monitoring the compliance of offered services as per the SLA. This paper presents the design of a trust evaluation framework that uses the compliance monitoring mechanism to determine the trustworthiness of service providers. The compliance values are computed and then processed using a technique known as the Improved Technique for Order of Preference by Similarity to Ideal Solution (Improved TOPSIS) to obtain trust on the service providers. Case study based approach has been followed to demonstrate the usability and the applicability of the proposed framework. Experiments have been performed using the real cloud data extracted from the Cloud Harmony reports. From the experimental results, it is clear that the proposed framework can be used in real cloud environments to determine the trustworthiness of service providers by employing the real time monitoring of their services.

Introduction to the Special Issue on Principles and Practices in Multi-Agent Systems

Agent-based Computing addresses the challenges in managing distributed computing systems and networks through monitoring, communication, consensus-based decision-making and coordinated actuation. As a result, intelligent agents and multi-agent systems have demonstrated the capability to use intelligence, knowledge representation and reasoning, and other social metaphors like trust, game and institution, to not only address real-world problems in a human-like way but to transcend human performance. This has had a transformative impact on many application domains, particularly e-commerce, but also on planning, logistics, manufacturing, robotics, decision support, transportation, entertainment, emergency relief and disaster management, and data mining and analytics. As one of the largest and still growing research fields of Computer Science, agent-based computing today remains a unique enabler of inter-, multi- and trans-disciplinary research. This special issue provides a selection of papers concerning the state-of-the-art research in multi-agent systems. Although the idea was initiated at 17th International Conference on Principles and Practice of Multi-Agent Systems, an open call allowed any researcher working on related topics to submit a paper for review. This special issue features four articles. The article entitled Combining PosoMAS Method Content with Scrum: Agile Software Engineering for Open Self-Organising Systems by Jan-Philipp Steghoefer, Hella Seebach, Benedikt Eberhardinger, Michael Huebschmann, Wolfgang Reif proposes PosoMAS-Scrum, an agile agent-oriented software engineering process for developing large-scale open self-organising systems. The authors also demonstrate how this method can be applied in a development effort and compare it with existing approaches. The article entitled Modelling Dynamic Normative Understanding in Agent Societies by Christopher Konstantin Frantz, Martin K. Purvis, Bastin Tony Roy Savarimuthu, Mariusz Nowostawski proposes an approach to build up normative understanding from the bottom up without using any prior knowledge. Their approach combines both existing institution representations (the structure) with a norm identification process. The article entitled A Multi-Agent Approach for Trust-based Service Discovery and Selection in Social Networks by Amine Louati, Joyce El Haddad, Suzanne Pinson proposes an approach to use multi-agent systems to model the service discovery and selection process. The authors present a trust model for social networks which supports agents in determining trustworthy service providers and allows agents to reason about trust in their distributed decision-making process. The article entitled Multi-Objective Distributed Constraint Optimization using Semi-Rings by Graham Billiau, Chee Fon Chang and Aditya Ghose proposes an extended Support Based Distributed Optimization algorithm to support Multi-Objective Distributed Constraint Optimization problem. The authors also demonstrate that by building a new DCOP definition using an idempotent semiring to measure the cost/utility of a solution, this approach is able to solve multiple objectives without a total pre-order over the set of solutions. We would like to thank the editorial board of SCPE for the chance of arranging this special issue, and all the reviewers for their hard work.

A novel cloud auditor based trust management framework for cloud computing

The adoption of cloud computing transfers control of resources to Cloud Service Providers CSPs. This gives rise to a variety of security and privacy issues. It has become incredibly complex for CCs to make distinction among similar kinds of services offered by different service providers. The proposed framework evaluates trustworthiness of service providers by evaluating compliance between monitored Quality of Service QoS parameters and QoS agreed in Service Level Agreement SLA. The compliance values are processed using a technique known as analytical hierarchical process to obtain the trust of service providers. To validate the design objective of framework, i.e. the formation of trust by evaluating compliance of services, a case-study-based approach has been followed. The proposed framework can be employed in real cloud environment to establish trust which can help CCs in selecting the most trustworthy service providers in this relatively new distributed computing environment.

A novel cloud auditor based trust management framework for cloud computing

The adoption of cloud computing transfers control of resources to Cloud Service Providers (CSPs). This gives rise to a variety of security and privacy issues. It has become incredibly complex for CCs to make distinction among similar kinds of services offered by different service providers. The proposed framework evaluates trustworthiness of service providers by evaluating compliance between monitored Quality of Service (QoS) parameters and QoS agreed in Service Level Agreement (SLA). The compliance values are processed using a technique known as analytical hierarchical process to obtain the trust of service providers. To validate the design objective of framework, i.e. the formation of trust by evaluating compliance of services, a case-study-based approach has been followed. The proposed framework can be employed in real cloud environment to establish trust which can help CCs in selecting the most trustworthy service providers in this relatively new distributed computing environment.

Trust Evaluation via Large-Scale Complex Service-Oriented Online Social Networks

Service-oriented online social networks (SOSNs) are emerging ubiquitous platforms for numerous services where service consumers require the selection of trustworthy service providers who are unknown to them before invoking services with the aid of other intermediate participants. Under this circumstance, evaluation of the trust level of the service provider along the social trust paths from the service consumer to the service provider is required. To this end, selection of the optimal social trust path (OSTP) that can yield the most trustworthy evaluation result is a prerequisite. While existing single-trust-value methods can provide good but simple information to service consumers, more trust information, such as social intimacy degree between participants and role impact factor of intermediate participants, should be considered to represent the trust level of a service provider more comprehensively. When more trust information is considered, OSTP selection will become an NP-complete problem. In this paper, we propose path integral Monte Carlo quantum annealing (PIMCQA)-based OSTP (PIMCQA_OSTP) selection algorithm for complex SOSNs. PIMCQA_OSTP serves as the very first quantum inspired OSTP selection algorithm in complex SOSNs. Due to that quantum mechanics work with wave functions that can sample different regions of phase space equally well, and quantum systems can tunnel through classically impenetrable potential barriers between energy valleys, PIMCQA_OSTP shows its outstanding search ability and outperforms existing methods. Results of experiments on a real dataset of online social networks verify that PIMCQA_OSTP is a promising tool and is especially fit for complex SOSNs.

A Trust Evaluation Model to Recommend a Service Provider to a Customer in Cloud Environment

In this era of cloud computing, there is a need to create some sense of security in customer’s mind before they can transfer their critical data on cloud. However, this trust establishment between a customer and service provider is a difficult task. Trust depends on intuitive understanding of a human being. Thus, evaluation of trust remains a major issue while making headway towards Cloud Computing. There must be a trusted third party which can help the customer to select a trustworthy service provider from a large pool of providers. In this paper, a trust evaluation model is presented that can be taken as a base to establish trust on service providers. This model recommends a service provider to customer according to his requirements. It evaluates trust from different perspectives. It considers feedbacks from customers, past experience of customer with service provider as well as results of monitoring done by third party to evaluate trust factor. The model has been simulated and the results show that the proposed model is effectual and adaptable to customers’ needs and priorities.

NGOs: Gaining Policy Influence Through Collaborative Networks

Faith-based organizations have been shown to have varying relevance and saliency in urban politics. Past research typically focuses on non-governmental organizations (NGOs) as intermediary organizations. The discussions revolve around the distinctly political, financial or administrative/service providing capacities of NGOs. Less explored are the unintended political consequences of their activities.Like their secular counterparts, faith-based organizations can play an essential role in the delivery of local public goods and services. They can become a linchpin of governance, providing much needed social support structures. Faith-based and other non-governmental organizations can act in concert with or under the devolved authority of the government. They can also fill gaps left by both the government and the market. This paper contends that by being consistent and trustworthy service providers, NGOs may change the landscape of stakeholders involved in urban politics. Indeed, even faith-based and secular NGOs that do not explicitly seek to influence policy may do so through the networks in which they affiliate and the entities with which they collaborate to carry out their primary functions.The paper is based on a nested research design with quantitative and qualitative data from public documents, surveys and interviews from several different types of communities, government entities and service providers. This paper examines the degree and extent to which NGOs work with the government and other entities to provide services and how that positions them to influence policy in Michigan’s two largest urban centers. It demonstrates the importance of collaborative networks for a range of governance functions. The paper also illuminates how service provision allows NGOs, faith-based and secular, to gain entree to local political networks and systems and correspondingly parlay that political access into policy influence.

UNCERTAINTY-BASED TRUST ESTIMATION IN A MULTI-VALUED TRUST ENVIRONMENT

Despite the widespread usage of the evaluation mediums for online services by the clients, there is a requirement for a trust evaluation tool that provides the clients with the degree of trustworthiness of the service providers. Such a tool can provide increased familiarity with unknown third party entities, e.g. service providers, especially when those entities neither project completely trustworthy nor totally untrustworthy behaviour. Indeed, developing some metrics for trust evaluation under uncertainty can come handy, e.g., for customers interested in evaluating the trustworthiness of an unknown service provider throughout queries to other customers of unknown reliability. In this research, we propose an evaluation metric to estimate the degree of trustworthiness of an unknown agent, say aD, through the information acquired through a group of agents who have interacted with agent aD. This group of agents is assumed to have an unknown degree of reliability. In order to tackle the uncertainty associated with the trust of these set of unknown agents, we suggest to use possibility distributions. Later, we introduce a new certainty metric to measure the degree of agreement in the information reported by the group of agents in A on agent aD. Fusion rules are then used to measure an estimation of the agent aD’s degree of trustworthiness. To the best of our knowledge, this is the first work that estimates trust, out of empirical data, subject to some uncertainty, in a discrete multi-valued trust domain. Finally, numerical experiments are presented to validate the proposed tools and metrics.

Finding the Optimal Social Trust Path for the Selection of Trustworthy Service Providers in Complex Social Networks

Online Social networks have provided the infrastructure for a number of emerging applications in recent years, e.g., for the recommendation of service providers or the recommendation of files as services. In these applications, trust is one of the most important factors in decision making by a service consumer, requiring the evaluation of the trustworthiness of a service provider along the social trust paths from a service consumer to the service provider. However, there are usually many social trust paths between two participants who are unknown to one another. In addition, some social information, such as social relationships between participants and the recommendation roles of participants, has significant influence on trust evaluation but has been neglected in existing studies of online social networks. Furthermore, it is a challenging problem to search the optimal social trust path that can yield the most trustworthy evaluation result and satisfy a service consumer's trust evaluation criteria based on social information. In this paper, we first present a novel complex social network structure incorporating trust, social relationships and recommendation roles, and introduce a new concept, Quality of Trust (QoT), containing the above social information as attributes. We then model the optimal social trust path selection problem with multiple end-to-end QoT constraints as a Multiconstrained Optimal Path (MCOP) selection problem, which is shown to be NP-Complete. To deal with this challenging problem, we propose a novel Multiple Foreseen Path-Based Heuristic algorithm MFPB-HOSTP for the Optimal Social Trust Path selection, where multiple backward local social trust paths (BLPs) are identified and concatenated with one Forward Local Path (FLP), forming multiple foreseen paths. Our strategy could not only help avoid failed feasibility estimation in path selection in certain cases, but also increase the chances of delivering a near-optimal solution with high quality. The results of our experiments conducted on a real data set of online social networks illustrate that MFPB-HOSTP algorithm can efficiently identify the social trust paths with better quality than our previously proposed H_OSTP algorithm that outperforms prior algorithms for the MCOP selection problem.