Browsing by Author "Bisdikian, C."

Now showing 1 - 3 of 3

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Inference management, trust and obfuscation principles for quality of information in emerging pervasive environments
(Elsevier, 2014-04) Bisdikian, C.; Gibson, C.; Chakraborty, S.; Srivastava, M. B.; Şensoy, Murat; Norman, T. J.; Computer Science; ŞENSOY, Murat
The emergence of large scale, distributed, sensor-enabled, machine-to-machine pervasive applications necessitates engaging with providers of information on demand to collect the information, of varying quality levels, to be used to infer about the state of the world and decide actions in response. In these highly fluid operational environments, involving information providers and consumers of various degrees of trust and intentions, information transformation, such as obfuscation, is used to manage the inferences that could be made to protect providers from misuses of the information they share, while still providing benefits to their information consumers. In this paper, we develop the initial principles for relating to inference management and the role that trust and obfuscation plays in it within the context of this emerging breed of applications. We start by extending the definitions of trust and obfuscation into this emerging application space. We, then, highlight their role as we move from the tightly-coupled to loosely-coupled sensory-inference systems and describe how quality, value and risk of information relate in collaborative and adversarial systems. Next, we discuss quality distortion illustrated through a human activity recognition sensory system. We then present a system architecture to support an inference firewall capability in a publish/subscribe system for sensory information and conclude with a discussion and closing remarks.
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Reasoning under uncertainty: variations of subjective logic deduction
(IEEE, 2013) Kaplan, L. M.; Şensoy, Murat; Tang, Y.; Chakraborty, S.; Bisdikian, C.; de Mel, G.; Computer Science; ŞENSOY, Murat
This work develops alternatives to the classical subjective logic deduction operator. Given antecedent and consequent propositions, the new operators form opinions of the consequent that match the variance of the consequent posterior distribution given opinions on the antecedent and the conditional rules connecting the antecedent with the consequent. As a result, the uncertainty of the consequent actually map to the spread for the probability projection of the opinion. Monte Carlo simulations demonstrate this connection for the new operators. Finally, the work uses Monte Carlo simulations to evaluate the quality of fusing opinions from multiple agents before and after deduction.
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TIDY: A trust-based approach to information fusion through diversity
(IEEE, 2013) Etuk, A.; Norman, T. J.; Şensoy, Murat; Bisdikian, C.; Srivatsa, M.; Computer Science; ŞENSOY, Murat
Trust and reputation are significant components in open dynamic systems for making informed and reliable decisions. State-of-the-art information fusion models that exploit these mechanisms generally rely on reports from as many sources as possible. Situations exist, however, where seeking evidence from all possible sources is unrealistic. Querying information sources is costly especially in resource-constrained environments, in terms of time and bandwidth. In addition, reports from multiple sources expose one to the risk of double-counting evidence, introducing an extra challenge of distinguishing fact from rumour. This paper describes TIDY (Trust-based Information fusion through DiversitY), a trust-based approach to information fusion that exploits diversity among information sources in order to select a small number of candidates to query for evidence, and to minimise the effect of correlated evidence and bias. We demonstrate that reliable decisions can be reached using evidence from small groups of individuals. We show empirically that our approach is robust in contexts of variable trust in information sources, and to a degree of deception.