Page 71 - Trust in ICT 2017

Trust in ICT 1

and edges denote a social relationship between people), or across paths of trust (where two parties
may not have direct trust information about each other, and must rely on a third party).
Recommendations are trust decisions made by other users, and combining these decisions to
synthesize a new one, often personalized, is another commonly addressed problem.

• General models of trust: There is a wealth of research on modelling and defining trust, its
prerequisites, conditions, components, and consequences. Trust models are useful for analysing
human and agenized trust decisions and for operationalizing computable models of trust. Work in
modelling trust describes values or factors that play a role in computing trust, and leans more on
work in psychology and sociology for a decomposition of what trust comprises. Modelling research
ranges from simple access control polices (which specify who to trust to access data or resources)
to analyses of competence, beliefs, risk, importance, utility, etc. These subcomponents underlying
trust help our understanding of the more subtle and complex aspects of composing, capturing, and
using trust in a computational setting.
A model of trust should capture and relate essential aspects of the trusts. While all three subcategories of
trust have been researched, it is well-accepted that in a social world, trust is modelled as reputation-based
approach. To express trust and reputation information ontologies are usually used, allowing for expression
and quantification of trust for use in algorithms to make a trust decision about any two entities [65].
8.2.1 Develop a trust model for a specific use case

Several interesting trust models and also systems, such as PolicyMaker, KeyNote and REFEREE have emerged.
However, the focus has been on more comprehensive and concrete system having wider trust management
elements, such as Poblano, Free Haven, SULTAN, TERM and SECURE.

8.2.1.1 Trust Networks on Sematic Webs
Golbeck first referred to such model as a Web-of-Trust. A Web-of-Trust is a directed-edge network between
a group of entities (or resources), within which each link carries a trust value and, assuming a transitivity of
trust, reputation can be collected and inferred for each single individual across such network. Within the
context of Web-of-Trust, reputation can be defined as a measure of trust, within which individuals can gather
and maintain reputation of other individuals across the network.
There are many measures of "trust" within a social network. It is common in a network that trust is based
simply on knowing someone. By treating a "Person" as a node, and the "knows" relationship as an edge, an
undirected graph emerges. If A does not know B, but some of A's friends know B, A is "close" to knowing B in
some sense. Many existing networks take this measure of closeness into account. We may, for example,
reasonably trust a person with a small Erdos number to have a stronger knowledge of graph theory than
someone with a large or infinite number [66].
Techniques developed to study naturally occurring social networks apply to these networks derived from the
semantic web. Small world models describe a number of algorithms for understanding relationships between
nodes. The same algorithms that model the spread of disease in physical social networks, can be used to
track the spread of viruses via email.
For trust, however, there are several other factors to consider. Edges in a trust network are directed. A may
trust B, but B may not trust A back. Edges are also weighted with some measure of the trust between two
people. By building such a network, it is possible to infer how much A should trust an unknown individual
based on how much A's friends and friends-of-friends trust that person. Using the edges that exist in the
graph, we can infer an estimation of the weight of a non-existent edge.

8.2.1.2 Beta Reputation System (BRS) [67]
Beta Reputation System (BRS) uses the expected value of the beta distribution to represent trust. Because of
this, its trust degrees are real numbers from [0, 1]. BRS computes trust from agent’s own experiences and
from opinions from third-parties. Such information comes in the form of 2-tuples <r,s> that represent the
amount of positive and negative feedback, respectively.

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