Difference between revisions of "Trust"

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Trust and confidence are becoming key issues in diverse applications such as ecommerce, social networks, semantic sensor web, semantic web information retrieval systems, etc. Both humans and machines use some form of trust to make informed and reliable decisions before acting. In this research, we reviewed drawbacks of existing approaches to trust and then  proposed a new local framework to explore two different kinds of trust among agents, namely, referral trust and functional trust. These have been modeled using local partial orders, to enable qualitative trust personalization. The proposed approach formalizes reasoning with trust, distinguishing between direct and inferred trust. It is also capable of dealing with general trust networks with cycles.
 
Trust and confidence are becoming key issues in diverse applications such as ecommerce, social networks, semantic sensor web, semantic web information retrieval systems, etc. Both humans and machines use some form of trust to make informed and reliable decisions before acting. In this research, we reviewed drawbacks of existing approaches to trust and then  proposed a new local framework to explore two different kinds of trust among agents, namely, referral trust and functional trust. These have been modeled using local partial orders, to enable qualitative trust personalization. The proposed approach formalizes reasoning with trust, distinguishing between direct and inferred trust. It is also capable of dealing with general trust networks with cycles.
  
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===Perception and Analysis of Sensor Data===
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===Trust Ontology===
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Currently, there are many sensors collecting information about our environment, leading to an overwhelming number of observations that must be analyzed and explained in order to achieve situation awareness. As perceptual beings, we are also constantly inundated with sensory data; yet we are able to make sense out of our environment with relative ease. This is due, in part, to the bi-directional information flow between our sensory organs and analytical brain. Drawing inspiration from cognitive models of perception, we can improve machine perception by allowing communication from processes that analyze observations to processes that generate observations. Such a perceptual system provides effective utilization of resources by decreasing the cost and number of observations needed for achieving situation awareness.
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While we all have an intuitive notion of trust, the literature is scattered with
 +
a wide assortment of differing definitions and descriptions; often
 +
these descriptions are highly dependent on a single domain or
 +
application of interest. In addition, they often discuss orthogonal
 +
aspects of trust while continuing to use the general term “trust”.
 +
In order to make sense of the situation, we have developed an
 +
ontology of trust that integrates and relates its various aspects
 +
into a single model.
 
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Revision as of 14:26, 16 November 2010

Trust in Interpersonal, Social, and Sensor Networks

Trust relationships occur naturally in many diverse contexts such as ecommerce, social interactions, social networks, ad hoc mobile networks, distributed systems, decision-support systems, (semantic) sensor web, emergency response scenarios, etc. As the connections and interactions between humans and/or machines (collectively called agents) evolve, and as the agents providing content and services become increasingly removed from the agents that consume them, miscreants attempt to corrupt, subvert or attack existing infrastructure. This in turn calls for support for robust trust inference (e.g., gleaning, aggregation, propagation) and update (also called trust management). Unfortunately, there is neither a universal notion of trust that is applicable to all domains nor a clear explication of its semantics in many situations. Because Web, social networking and sensor information often provide complementary and overlapping information about an activity or event that are critical for overall situational awareness, there is a unique need for developing an understanding of and techniques for managing trust that span all these information channels. Currently, we are pursuing research on trust and trustworthiness issues in interpersonal, social, and sensor networks, to potentially unify and integrate them for exploiting their complementary strengths.

Research Topics

Trust Model/Framework for Social and Sensor Web

Trust and confidence are becoming key issues in diverse applications such as ecommerce, social networks, semantic sensor web, semantic web information retrieval systems, etc. Both humans and machines use some form of trust to make informed and reliable decisions before acting. In this research, we reviewed drawbacks of existing approaches to trust and then proposed a new local framework to explore two different kinds of trust among agents, namely, referral trust and functional trust. These have been modeled using local partial orders, to enable qualitative trust personalization. The proposed approach formalizes reasoning with trust, distinguishing between direct and inferred trust. It is also capable of dealing with general trust networks with cycles.

Trust Ontology

While we all have an intuitive notion of trust, the literature is scattered with a wide assortment of differing definitions and descriptions; often these descriptions are highly dependent on a single domain or application of interest. In addition, they often discuss orthogonal aspects of trust while continuing to use the general term “trust”. In order to make sense of the situation, we have developed an ontology of trust that integrates and relates its various aspects into a single model.

People


Publications


Presentations


Ontologies


Prototypes, Demos and Tools

  • Trusted Perception Cycle: demo


Related Material

Computing for Human Experience


Contact Information: Pramod Anantharam

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