Page 65 - Trust in ICT 2017

Trust in ICT 1

Figure 14 – Trust components interactions in the trust service platform

• Trust Agent: used to collect trust-related data from physical, cyber and social ICT domains. The data
could be trust agents or opinions of entities as recommendation or feedbacks to other entities,
applications or services.
• Trust Broker: used to provide the trust knowledge to various type of applications and services in the
ICT ecosystem. It is required to register information such as knowledge, trust ontology or service
requirements prior to use the trust service platform.
• Trust Analysis and Management: Beside a part for collaborating with the Reputation System, all
trust-related mechanisms such as ontology-related manager, information model, reasoning
mechanisms, trust cloud infrastructure, Knowledge based trust evaluation mechanisms, and trust
calculation algorithms are implemented at this module.

7.4 Develop a framework for decision making in the trust analysis system of trustworthy ICT
Eco-system
Ongoing research agenda includes designing a fully automating trust decision making process under
dynamically changing ICT environment. In this regard different decision mechanisms can be observed in the
literature with different techniques.
Utility functions provide a natural and advantageous framework for achieving self-optimization in distributed
autonomic computing systems. In this regard, [50] introduced an architecture for incorporating utility
functions as part of the decision-making process of an autonomic system. Utility functions were shown to be
effective in handling reconfiguration decisions against multiple objectives.

In the context of autonomic trust computing, utility functions map possible states of an entity into scalar
values that quantify the desirability of a configuration as determined by user preferences. Given a utility
function, the autonomic system determines the most valuable system state and the means for reaching it. In
the approach proposed in [50], a utility calculator repeatedly computes the value that would be obtained
from each possible configuration. Despite their advantages, utility functions may suffer from complexity
issues as multiple dimensions scale depending on the evaluation method used. In contrast, although genetic
algorithms use fitness functions, which are akin to utility functions, the process of natural selection efficiently
guides the search process through the solution space.

The paper [51] proposes an approach to leverage genetic algorithms in the decision-making process of an
autonomic system. This approach enables a system to dynamically evolve reconfiguration plans at run time
in response to changing requirements and environmental conditions. A key feature of this approach is

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