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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 understanding and development of 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

A Local Qualitative Approach to Referral and Functional Trust

Trust is a key issue in diverse applications spanning 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 Trustworthiness Issues for Social and Sensor Web

We explore research issues relevant to trust and trustworthiness in social and sensor networks, and interactions and exchanges they promote. We advocate a balanced, iterative approach to trust that marries both theory and practice. On the theoretical side, we investigate models of trust to analyze and specify the nature of trust and trust computation. On the practical side, we propose to uncover aspects that provide a basis for trust formation and techniques to extract trust information from concrete social/sensor networks and interactions. We expect the development of formal models of trust and techniques to glean trust information from social media and sensor web to be fundamental enablers for applying semantic web technologies to trust management.

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.





Prototypes, Demos and Tools

  • Trusted Perception Cycle: demo
    This demo helps in visualizing the perception cycle (abductive inference) and reputation values computed for weather stations over a period of six days. Various features inferred from raw sensor data using the perception cycle are depicted with different colors of the bars and height of the bar represents the reputation value of each weather station. The demo shows all the inferred features and the way in which the reputation computation converges. Main contributions include: (1) Development and formalization of perception cycle (2) Implementation of a reputation system which used beta-pdf distribution to compute trust values.
  • Semantic Sensor Observation Service (MesoWest): demo

Related Material

Computing for Human Experience

Contact Information: Pramod Anantharam