7,530 research outputs found
A trust and reputation model based on bayesian network for web services
Trust and reputation for web services emerges as an important research issue in web service selection. Current web service trust models either do not integrate different important sources of trust (subjective and objective for example), or do not focus on satisfying different user’s requirements about different quality of service (QoS) attributes such as performance, availability etc. In this paper, we propose a Bayesian network trust and reputation model for web services that can overcome such limitations by considering several factors when assessing web services’ trust: direct opinion from the truster, user rating (subjective view) and QoS monitoring information (objective view). Our comprehensive approach also addresses the problems of users’ preferences and multiple QoSbased trust by specifying different conditions for the Bayesian network and targets at building a reasonable credibility model for the raters of web services
Trust and Reputation Management: a Probabilistic Approach
Software architectures of large-scale systems are perceptibly shifting towards employing open
and distributed computing. Web services emerged as autonomous and self-contained business applications that are published, found, and used over the web. These web services thus exist in an environment in which they interact among each other to achieve their goals. Two challenging tasks that govern the agents interactions have gained the attention of a large research community;
web service selection and composition. The explosion of the number of published web services contributed to the growth of large pools of similarly functional services. While this is vital for a competitive and healthy marketplace, it complicates the aforementioned tasks. Service consumers resort to non-functional characteristics of available service providers to decide which service to interact with. Therefore, to optimize both tasks and maximize the gain of all involved agents, it is essential to build the capability of modeling and predicting the quality of these agents.
In this thesis, we propose various trust and reputation models based on probabilistic approaches to address the web service selection and composition problems. These approaches consider the trustworthiness of a web service to be strongly tied to the outcomes of various quality of service metrics such as response time, throughput, and reliability. We represent these outcomes by a multinomial distribution whose parameters are learned using Bayesian inference which, given a likelihood function and a prior probability, derives the posterior probability. Since the likelihood, in this case, is a multinomial, a commonly used prior is the Dirichlet distribution. We propose, to
overcome several limitations of the Dirichlet, by applying two alternative priors such as the generalized
Dirichlet, and Beta-Liouville. Using these distributions, the learned parameters represent the probabilities of a web service to belong to each of the considered quality classes. These probabilities are consequently used to compute the trustworthiness of the evaluated web services and thus assisting consumers in the service selection process. Furthermore, after exploring the correlations
among various quality metrics using real data sets, we introduce a hybrid trust model that captures these correlations using both Dirichlet and generalized Dirichlet distributions. Given their covariance structures, the former performs better when modeling negative correlations while the latter yields better modeling of positive correlations. To handle composite services, we propose various trust approaches using Bayesian networks and mixture models of three different distributions; the multinomial Dirichlet, the multinomial generalized Dirichlet, and the multinomial Beta-Liouville. Specifically, we employ a Bayesian network classifier with a Beta- Liouville prior to enable the classification of the QoS of composite services given the QoS of its constituents. In addition, we extend the previous models to function in online settings. Therefore, we present a generalized-Dirichlet power steady model that predicts compositional time series. We similarly extend the Bayesian networks model by using the Voting EM algorithm. This extension enables the estimation of the networks parameters after each interaction with a composite web service. Furthermore, we propose an algorithm to estimate the reputation of web services. We extend this algorithm by leveraging the capabilities of various clustering and outlier detection techniques to deal with malicious feedback and various strategic behavior commonly performed by web services.
Alternatively, we suggest two data fusion methods for reputation feedback aggregation, namely, the covariance intersection and ellipsoidal intersection. These methods handle the dependency between the information that propagates through networks of interacting agents. They also avoid over confident estimates caused by redundant information. Finally, we present a reputation model for
agent-based web services grouped into communities of homogeneous functionalities. We exploit various clustering and anomaly detection techniques to analyze and identify the quality trends provided by each service. This model enables the master of each community to allocate the requests it receives to the web service that best fulfill the quality requirements of the service consumers. We evaluate the effectiveness of the proposed approaches using both simulated and real data
An Intelligent QoS Identification for Untrustworthy Web Services Via Two-phase Neural Networks
QoS identification for untrustworthy Web services is critical in QoS
management in the service computing since the performance of untrustworthy Web
services may result in QoS downgrade. The key issue is to intelligently learn
the characteristics of trustworthy Web services from different QoS levels, then
to identify the untrustworthy ones according to the characteristics of QoS
metrics. As one of the intelligent identification approaches, deep neural
network has emerged as a powerful technique in recent years. In this paper, we
propose a novel two-phase neural network model to identify the untrustworthy
Web services. In the first phase, Web services are collected from the published
QoS dataset. Then, we design a feedforward neural network model to build the
classifier for Web services with different QoS levels. In the second phase, we
employ a probabilistic neural network (PNN) model to identify the untrustworthy
Web services from each classification. The experimental results show the
proposed approach has 90.5% identification ratio far higher than other
competing approaches.Comment: 8 pages, 5 figure
Trust Strategies for the Semantic Web
Everyone agrees on the importance of enabling trust on the SemanticWebto ensure more efficient agent interaction. Current research on trust seems to focus on developing computational models, semantic representations, inference techniques, etc. However, little attention has been given to the plausible trust strategies or tactics that an agent can follow when interacting with other agents on the Semantic Web. In this paper we identify five most common strategies of trust and discuss their envisaged costs and benefits. The aim is to provide some guidelines to help system developers appreciate the risks and gains involved with each trust strategy
An efficient and versatile approach to trust and reputation using hierarchical Bayesian modelling
In many dynamic open systems, autonomous agents must interact with one another to achieve their goals. Such agents may be self-interested and, when trusted to perform an action, may betray that trust by not performing the action as required. Due to the scale and dynamism of these systems, agents will often need to interact with other agents with which they have little or no past experience. Each agent must therefore be capable of assessing and identifying reliable interaction partners, even if it has no personal experience with them. To this end, we present HABIT, a Hierarchical And Bayesian Inferred Trust model for assessing how much an agent should trust its peers based on direct and third party information. This model is robust in environments in which third party information is malicious, noisy, or otherwise inaccurate. Although existing approaches claim to achieve this, most rely on heuristics with little theoretical foundation. In contrast, HABIT is based exclusively on principled statistical techniques: it can cope with multiple discrete or continuous aspects of trustee behaviour; it does not restrict agents to using a single shared representation of behaviour; it can improve assessment by using any observed correlation between the behaviour of similar trustees or information sources; and it provides a pragmatic solution to the whitewasher problem (in which unreliable agents assume a new identity to avoid bad reputation). In this paper, we describe the theoretical aspects of HABIT, and present experimental results that demonstrate its ability to predict agent behaviour in both a simulated environment, and one based on data from a real-world webserver domain. In particular, these experiments show that HABIT can predict trustee performance based on multiple representations of behaviour, and is up to twice as accurate as BLADE, an existing state-of-the-art trust model that is both statistically principled and has been previously shown to outperform a number of other probabilistic trust models
Local and Global Trust Based on the Concept of Promises
We use the notion of a promise to define local trust between agents
possessing autonomous decision-making. An agent is trustworthy if it is
expected that it will keep a promise. This definition satisfies most
commonplace meanings of trust. Reputation is then an estimation of this
expectation value that is passed on from agent to agent.
Our definition distinguishes types of trust, for different behaviours, and
decouples the concept of agent reliability from the behaviour on which the
judgement is based. We show, however, that trust is fundamentally heuristic, as
it provides insufficient information for agents to make a rational judgement. A
global trustworthiness, or community trust can be defined by a proportional,
self-consistent voting process, as a weighted eigenvector-centrality function
of the promise theoretical graph
Asymptotically idempotent aggregation operators for trust management in multi-agent systems
The study of trust management in
multi-agent system, especially distributed,
has grown over the last
years. Trust is a complex subject
that has no general consensus in literature,
but has emerged the importance
of reasoning about it computationally.
Reputation systems takes
into consideration the history of an
entity’s actions/behavior in order to
compute trust, collecting and aggregating
ratings from members in a
community. In this scenario the aggregation
problem becomes fundamental,
in particular depending on
the environment. In this paper we
describe a technique based on a class
of asymptotically idempotent aggregation
operators, suitable particulary
for distributed anonymous environments
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
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A Framework for Trusted Services
An existing challenge when selecting services to be used in a service- based system is to be able to distinguish between good and bad services. In this paper we present a trust-based service selection framework. The framework uses a trust model that calculates the level of trust a user may have with a service based on past experience of the user with the service and feedback about the service received from other users. The model takes into account different levels of trust among users, different relationships between users, and different levels of importance that a user may have for certain quality aspects of a service. A prototype tool has been implemented to illustrate and evaluate the work. The trust model has been evaluated in terms of its capacity to adjust itself due to changes in user ratings and its robustness
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