36,141 research outputs found
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
The Reputation, Opinion, Credibility and Quality (ROCQ) Scheme
An implicit assumption of trust in the participants is at the basis of most Peer-to-Peer (P2P) networks. However, in practice, not all participants are benign or cooperative. Identifying such peers is critical to the smooth and effective functioning of a P2P network. In this paper, we present the ROCQ mechanism, a reputation-based trust management system that computes the trustworthiness of peers on the basis of transaction-based feedback. The ROCQ model combines four parameters: Reputation (R) or a peer's global trust rating, Opinion (O) formed by a peer's first-hand interactions, Credibility (C) of a reporting peer and Quality (Q) or the confidence a reporting peer puts on the judgement it provides. We then present a distributed implementation of our scheme over FreePastry, a structured P2P network. Experimental results considering different models for malicious behavior indicate the contexts in which the ROCQ scheme performs better than existing schemes
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: more than just ranking
The last years have seen a growing interest in collaborative systems like
electronic marketplaces and P2P file sharing systems where people are intended
to interact with other people. Those systems, however, are subject to security
and operational risks because of their open and distributed nature. Reputation
systems provide a mechanism to reduce such risks by building trust
relationships among entities and identifying malicious entities. A popular
reputation model is the so called flow-based model. Most existing reputation
systems based on such a model provide only a ranking, without absolute
reputation values; this makes it difficult to determine whether entities are
actually trustworthy or untrustworthy. In addition, those systems ignore a
significant part of the available information; as a consequence, reputation
values may not be accurate. In this paper, we present a flow-based reputation
metric that gives absolute values instead of merely a ranking. Our metric makes
use of all the available information. We study, both analytically and
numerically, the properties of the proposed metric and the effect of attacks on
reputation values
MORE: Merged Opinions Reputation Model
Reputation is generally defined as the opinion of a group on an aspect of a
thing. This paper presents a reputation model that follows a probabilistic
modelling of opinions based on three main concepts: (1) the value of an opinion
decays with time, (2) the reputation of the opinion source impacts the
reliability of the opinion, and (3) the certainty of the opinion impacts its
weight with respect to other opinions. Furthermore, the model is flexible with
its opinion sources: it may use explicit opinions or implicit opinions that can
be extracted from agent behavior in domains where explicit opinions are sparse.
We illustrate the latter with an approach to extract opinions from behavioral
information in the sports domain, focusing on football in particular. One of
the uses of a reputation model is predicting behavior. We take up the challenge
of predicting the behavior of football teams in football matches, which we
argue is a very interesting yet difficult approach for evaluating the model.Comment: 12th European Conference on Multi-Agent Systems (EUMAS 2014
Reputation-based trust evaluations through diversity
Non peer reviewedPostprin
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
ResEval: A Mashup Platform for Research Evaluation
Bibliometrics has changed out the way the research evaluation conducted, and it is widely used to evaluate research groups, individual research's, department and many more. However establishing fair criteria to evaluate the scientific community, as well as individual publications and researcher, is a tough task and constitutes a challenge that has not been achieved yet. This paper addresses the problem of research evaluation and introduces ResEval, a mashup platform that enables the creation of customize metrics and their computation in order to make the scientific evaluation easier. This platform addresses various problems with current approaches such as data incompleteness, flexibility in defining new metrics, fixed UI restrictions for the customization of metrics and to apply filters
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