619 research outputs found
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
FRTRUST: a fuzzy reputation based model for trust management in semantic P2P grids
Grid and peer-to-peer (P2P) networks are two ideal technologies for file
sharing. A P2P grid is a special case of grid networks in which P2P
communications are used for communication between nodes and trust management.
Use of this technology allows creation of a network with greater distribution
and scalability. Semantic grids have appeared as an expansion of grid networks
in which rich resource metadata are revealed and clearly handled. In a semantic
P2P grid, nodes are clustered into different groups based on the semantic
similarities between their services. This paper proposes a reputation model for
trust management in a semantic P2P Grid. We use fuzzy theory, in a trust
overlay network named FR TRUST that models the network structure and the
storage of reputation information. In fact we present a reputation collection
and computation system for semantic P2P Grids. The system uses fuzzy theory to
compute a peer trust level, which can be either: Low, Medium, or High. Our
experimental results demonstrate that FR TRUST combines low (and therefore
desirable) a good computational complexity with high ranking accuracy.Comment: 12 Pages, 10 Figures, 3 Tables, InderScience, International Journal
of Grid and Utility Computin
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
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
Fuzzy Based Trust Model for Peer to Peer Systems
Unknown nature of peer to peer system opens them to malicious actions. A fuzzy based trust model can create trust relationships among peers. Trust decisions are adaptive to modifications in trust between peers. A peer’s trustworthiness in giving services and recommendations are assessed in service and recommendation context. The model utilizes fuzzy logic to integrate eight trust evaluation factors into the reputation evaluation process for improving the efficiency and security of peer to peer system. The reputation and recommendation trust metric is combined for computing a global trust metric which helps in selecting the best service provider. In this manner peers develop a trust network in their vicinity without utilizing earlier information and can tone down attack of malicious peers
A Survey on Trust Computation in the Internet of Things
Internet of Things defines a large number of diverse entities and services which interconnect with each other and individually or cooperatively operate depending on context, conditions and environments, produce a huge personal and sensitive data. In this scenario, the satisfaction of privacy, security and trust plays a critical role in the success of the Internet of Things. Trust here can be considered as a key property to establish trustworthy and seamless connectivity among entities and to guarantee secure services and applications. The aim of this study is to provide a survey on various trust computation strategies and identify future trends in the field. We discuss trust computation methods under several aspects and provide comparison of the approaches based on trust features, performance, advantages, weaknesses and limitations of each strategy. Finally the research discuss on the gap of the trust literature and raise some research directions in trust computation in the Internet of Things
Class Based Multi Stage Encryption for Efficient Data Security in Cloud Environment Using Profile Data
The security issues in the cloud have been well studied. The data security has much importance in point of data owner. There are number of approaches presented earlier towards performance in data security in cloud. To overcome the issues, a class based multi stage encryption algorithm is presented in this paper. The method classifies the data into number of classes and different encryption scheme is used for different classes in different levels. Similarly, the user has been authenticated for their access and they have been classified into different categories. According to the user profile, the method restricts the access of user and based on the same, the method defines security measures. A system defined encryption methodology is used for encrypting the data. Moreover, the user has been returned with other encryption methods which can be decrypted by the user using their own key provided by the system. The proposed algorithm improves the performance of security and improves the data security
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