73,322 research outputs found
A flexible architecture for privacy-aware trust management
In service-oriented systems a constellation of services cooperate, sharing potentially sensitive information and responsibilities. Cooperation is only possible if the different participants trust each other. As trust may depend on many different factors, in a flexible framework for Trust Management (TM) trust must be computed by combining different types of information. In this paper we describe the TAS3 TM framework which integrates independent TM systems into a single trust decision point. The TM framework supports intricate combinations whilst still remaining easily extensible. It also provides a unified trust evaluation interface to the (authorization framework of the) services. We demonstrate the flexibility of the approach by integrating three distinct TM paradigms: reputation-based TM, credential-based TM, and Key Performance Indicator TM. Finally, we discuss privacy concerns in TM systems and the directions to be taken for the definition of a privacy-friendly TM architecture.\u
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
Data centric trust evaluation and prediction framework for IOT
© 2017 ITU. Application of trust principals in internet of things (IoT) has allowed to provide more trustworthy services among the corresponding stakeholders. The most common method of assessing trust in IoT applications is to estimate trust level of the end entities (entity-centric) relative to the trustor. In these systems, trust level of the data is assumed to be the same as the trust level of the data source. However, most of the IoT based systems are data centric and operate in dynamic environments, which need immediate actions without waiting for a trust report from end entities. We address this challenge by extending our previous proposals on trust establishment for entities based on their reputation, experience and knowledge, to trust estimation of data items [1-3]. First, we present a hybrid trust framework for evaluating both data trust and entity trust, which will be enhanced as a standardization for future data driven society. The modules including data trust metric extraction, data trust aggregation, evaluation and prediction are elaborated inside the proposed framework. Finally, a possible design model is described to implement the proposed ideas
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Opinion Model Based Security Reputation Enabling Cloud Broker Architecture
A Formal Framework for Modeling Trust and Reputation in Collective Adaptive Systems
Trust and reputation models for distributed, collaborative systems have been
studied and applied in several domains, in order to stimulate cooperation while
preventing selfish and malicious behaviors. Nonetheless, such models have
received less attention in the process of specifying and analyzing formally the
functionalities of the systems mentioned above. The objective of this paper is
to define a process algebraic framework for the modeling of systems that use
(i) trust and reputation to govern the interactions among nodes, and (ii)
communication models characterized by a high level of adaptiveness and
flexibility. Hence, we propose a formalism for verifying, through model
checking techniques, the robustness of these systems with respect to the
typical attacks conducted against webs of trust.Comment: In Proceedings FORECAST 2016, arXiv:1607.0200
Modelling and testing consumer trust dimensions in e-commerce
Prior research has found trust to play a significant role in shaping purchase intentions of a consumer. However there has been limited research where consumer trust dimensions have been empirically defined and tested. In this paper we empirically test a path model such that Internet vendors would have adequate solutions to increase trust. The path model presented in this paper measures the three main dimensions of trust, i.e. competence, integrity, and benevolence. And assesses the influence of overall trust of consumers. The paper also analyses how various sources of trust, i.e. consumer characteristics, firm characteristic, website infrastructure and interactions with consumers, influence dimensions of trust. The model is tested using 365 valid responses. Findings suggest that consumers with high overall trust demonstrate a higher intention to purchase online
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
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
Flow-based reputation with uncertainty: Evidence-Based Subjective Logic
The concept of reputation is widely used as a measure of trustworthiness
based on ratings from members in a community. The adoption of reputation
systems, however, relies on their ability to capture the actual trustworthiness
of a target. Several reputation models for aggregating trust information have
been proposed in the literature. The choice of model has an impact on the
reliability of the aggregated trust information as well as on the procedure
used to compute reputations. Two prominent models are flow-based reputation
(e.g., EigenTrust, PageRank) and Subjective Logic based reputation. Flow-based
models provide an automated method to aggregate trust information, but they are
not able to express the level of uncertainty in the information. In contrast,
Subjective Logic extends probabilistic models with an explicit notion of
uncertainty, but the calculation of reputation depends on the structure of the
trust network and often requires information to be discarded. These are severe
drawbacks.
In this work, we observe that the `opinion discounting' operation in
Subjective Logic has a number of basic problems. We resolve these problems by
providing a new discounting operator that describes the flow of evidence from
one party to another. The adoption of our discounting rule results in a
consistent Subjective Logic algebra that is entirely based on the handling of
evidence. We show that the new algebra enables the construction of an automated
reputation assessment procedure for arbitrary trust networks, where the
calculation no longer depends on the structure of the network, and does not
need to throw away any information. Thus, we obtain the best of both worlds:
flow-based reputation and consistent handling of uncertainties
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