An Evaluation Framework for Reputation Management Systems

Reputation management (RM) is employed in distributed and peer-to-peer networks to help users compute a measure of trust in other users based on initial belief, observed behavior, and run-time feedback. These trust values influence how, or with whom, a user will interact. Existing literature on RM focuses primarily on algorithm development, not comparative analysis. To remedy this, we propose an evaluation framework based on the trace-simulator paradigm. Trace file generation emulates a variety of network configurations, and particular attention is given to modeling malicious user behavior. Simulation is trace-based and incremental trust calculation techniques are developed to allow experimentation with networks of substantial size. The described framework is available as open source so that researchers can evaluate the effectiveness of other reputation management techniques and/or extend functionality. This chapter reports on our framework’s design decisions. Our goal being to build a general-purpose simulator, we have the opportunity to characterize the breadth of existing RM systems. Further, we demonstrate our tool using two reputation algorithms (EigenTrust and a modified TNA-SL) under varied network conditions. Our analysis permits us to make claims about the algorithms’ comparative merits. We conclude that such systems, assuming their distribution is secure, are highly effective at managing trust, even against adversarial collectives

Report on a Working Session on Security in Wireless Ad Hoc Networks

No abstrac

Cooperation of Nodes. In: L. Buttyan and J.-P. Hubaux (eds.), Report on a Working Session on Security in Wireless Ad Hoc Networks

In mobile ad-hoc networks nodes need to cooperate to communicate, but there are many reasons for non-cooperation. Saving power or preventing other nodes from obstructing a service are merely selfish reasons for non-cooperation, whereas nodes may also actively and maliciously deny service or divert traffic for all sorts of attacks. However, without an infrastructure to rely on, nodes depend on each other`s cooperation. In game-theoretic terms, this is a dilemma. The dominating strategy for individual nodes is not to cooperate, as cooperation consumes resources and it might result in a disadvantage. But if every node follows that strategy, the outcome is undesirable for everyone as it results in a non functional or entirely absent network. Our goals are to increase cooperation by proactively giving selfish nodes an incentive to cooperate, as well as reactively isolate selfish or malicious nodes such that they cannot continue their misbehavior. To make cooperation in mobile ad-hoc networks attractive we have to make sure that selfish behavior, i.e., a behavior that maximizes the utility of a node, leads to an outcome that is also beneficial for the network

Trust-based algorithms for fusing crowdsourced estimates of continuous quantities

Crowdsourcing has provided a viable way of gathering information at unprecedented volumes and speed by engaging individuals to perform simple micro–tasks. In particular, the crowdsourcing paradigm has been successfully applied to participatory sensing, in which the users perform sensing tasks and provide data using their mobile devices. In this way, people can help solve complex environmental sensing tasks, such as weather monitoring, nuclear radiation monitoring and cell tower mapping, in a highly decentralised and parallelised fashion. Traditionally, crowdsourcing technologies were primarily used for gathering data for classifications and image labelling tasks. In contrast, such crowd–based participatory sensing poses new challenges that relate to (i) dealing with human–reported sensor data that are available in the form of continuous estimates of an observed quantity such as a location, a temperature or a sound reading, (ii) dealing with possible spatial and temporal correlations within the data and (ii) issues of data trustworthiness due to the unknown capabilities and incentives of the participants and their devices. Solutions to these challenges need to be able to combine the data provided by multiple users to ensure the accuracy and the validity of the aggregated results. With this in mind, our goal is to provide methods to better aid the aggregation process of crowd–reported sensor estimates of continuous quantities when data are provided by individuals of varying trustworthiness. To achieve this, we develop a trust–based in- formation fusion framework that incorporates latent trustworthiness traits of the users within the data fusion process. Through this framework, we develop a set of four novel algorithms (MaxTrust, BACE, TrustGP and TrustLGCP) to compute reliable aggregations of the users’ reports in both the settings of observing a stationary quantity (Max- Trust and BACE) and a spatially distributed phenomenon (TrustGP and TrustLGCP). The key feature of all these algorithm is the ability of (i) learning the trustworthiness of each individual who provide the data and (ii) exploit this latent user’s trustworthiness information to compute a more accurate fused estimate. In particular, this is achieved by using a probabilistic framework that allows our methods to simultaneously learn the fused estimate and the users’ trustworthiness from the crowd reports. We validate our algorithms in four key application areas (cell tower mapping, WiFi networks mapping, nuclear radiation monitoring and disaster response) that demonstrate the practical impact of our framework to achieve substantially more accurate and informative predictions compared to the existing fusion methods. We expect that results of this thesis will allow to build more reliable data fusion algorithms for the broad class of human–centred information systems (e.g., recommendation systems, peer reviewing systems, student grading tools) that are based on making decisions upon subjective opinions provided by their users

Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement

Volume measurement plays an important role in the production and processing of food products. Various methods have been proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images. Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the water displacement method. In addition, the proposed method is more accurate and faster than the space carving method

Proceedings of the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

This book is a collection of 15 reviewed technical reports summarizing the presentations at the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory. The covered topics include image processing, optical signal processing, visual inspection, pattern recognition and classification, human-machine interaction, world and situation modeling, autonomous system localization and mapping, information fusion, and trust propagation in sensor networks

Game Theory Relaunched

The game is on. Do you know how to play? Game theory sets out to explore what can be said about making decisions which go beyond accepting the rules of a game. Since 1942, a well elaborated mathematical apparatus has been developed to do so; but there is more. During the last three decades game theoretic reasoning has popped up in many other fields as well - from engineering to biology and psychology. New simulation tools and network analysis have made game theory omnipresent these days. This book collects recent research papers in game theory, which come from diverse scientific communities all across the world; they combine many different fields like economics, politics, history, engineering, mathematics, physics, and psychology. All of them have as a common denominator some method of game theory. Enjoy