5,805 research outputs found

    Trust dynamics for collaborative global computing

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    Recent advances in networking technology have increased the potential for dynamic enterprise collaborations between an open set of entities on a global scale. The security of these collaborations is a major concern, and requires novel approaches suited to this new environment to be developed. Trust management appears to be a promising approach. Due to the dynamic nature of these collaborations,dynamism in the formation, evolution and exploitation of trust is essential. In this paper we explore the properties of trust dynamics in this context. Trust is formed and evolves according to personal experience and recommendations. The properties of trust dynamics are expressed through a formal model of trust. Specific examples, based on an e-purse application scenario are used to demonstrate these properties

    A formal model of trust lifecycle management

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    The rapid development of collaborative environments over the internet has highlighted new concerns over security and trust in such global computing systems. The global computing infrastructure poses an issue of uncertainty about the potential collaborators. Reaching a trusting decision in such environments encompasses both risk and trust assessments. While much work has been done in terms of modelling trust, the investigation of the management of trust lifecycle issues with consideration of both trust and risk is less examined. Our previous work addressed the dynamic aspects of trust lifecycle with a consideration of trust formation, exploitation, and evolution. In this paper we provide an approach for formalizing these aspects. As part of the formalization of the trust lifecycle,we introduce a notion of attraction to model the effect of new pieces of evidence on our opinion. The formalization described in this paper constitutes the basis of ongoing work to investigate the properties of the model

    Mean Field Equilibrium in Dynamic Games with Complementarities

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    We study a class of stochastic dynamic games that exhibit strategic complementarities between players; formally, in the games we consider, the payoff of a player has increasing differences between her own state and the empirical distribution of the states of other players. Such games can be used to model a diverse set of applications, including network security models, recommender systems, and dynamic search in markets. Stochastic games are generally difficult to analyze, and these difficulties are only exacerbated when the number of players is large (as might be the case in the preceding examples). We consider an approximation methodology called mean field equilibrium to study these games. In such an equilibrium, each player reacts to only the long run average state of other players. We find necessary conditions for the existence of a mean field equilibrium in such games. Furthermore, as a simple consequence of this existence theorem, we obtain several natural monotonicity properties. We show that there exist a "largest" and a "smallest" equilibrium among all those where the equilibrium strategy used by a player is nondecreasing, and we also show that players converge to each of these equilibria via natural myopic learning dynamics; as we argue, these dynamics are more reasonable than the standard best response dynamics. We also provide sensitivity results, where we quantify how the equilibria of such games move in response to changes in parameters of the game (e.g., the introduction of incentives to players).Comment: 56 pages, 5 figure

    Processing and Linking Audio Events in Large Multimedia Archives: The EU inEvent Project

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    In the inEvent EU project [1], we aim at structuring, retrieving, and sharing large archives of networked, and dynamically changing, multimedia recordings, mainly consisting of meetings, videoconferences, and lectures. More specifically, we are developing an integrated system that performs audiovisual processing of multimedia recordings, and labels them in terms of interconnected “hyper-events ” (a notion inspired from hyper-texts). Each hyper-event is composed of simpler facets, including audio-video recordings and metadata, which are then easier to search, retrieve and share. In the present paper, we mainly cover the audio processing aspects of the system, including speech recognition, speaker diarization and linking (across recordings), the use of these features for hyper-event indexing and recommendation, and the search portal. We present initial results for feature extraction from lecture recordings using the TED talks. Index Terms: Networked multimedia events; audio processing: speech recognition; speaker diarization and linking; multimedia indexing and searching; hyper-events. 1

    Context-based Grouping and Recommendation in MANETs

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    International audienceWe propose in this chapter a context grouping mechanism for context distribution over MANETs. Context distribution is becoming a key aspect for successful context-aware applications in mobile and ubiquitous computing environments. Such applications need, for adaptation purposes, context information that is acquired by multiple context sensors distributed over the environment. Nevertheless, applications are not interested in all available context information. Context distribution mechanisms have to cope with the dynamicity that characterizes MANETs and also prevent context information to be delivered to nodes (and applications) that are not interested in it. Our grouping mechanism organizes the distribution of context information in groups whose definition is context based: each context group is defined based on a criteria set (e.g. the shared location and interest) and has a dissemination set, which controls the information that can be shared in the group. We propose a personalized and dynamic way of defining and joining groups by providing a lattice-based classification and recommendation mechanism that analyzes the interrelations between groups and users, and recommend new groups to users, based on the interests and preferences of the user
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