Protecting Participatory Sensing Using Cloud Based Trust Management System against Sybil Attack

[[abstract]]Participatory sensing is an innovative model in mobile sensing network which allows volunteers to collect and share information from their local environment by using mobile phones. Unlike other participatory sensing application challenges which consider user privacy and data trustworthiness, we consider the network trustworthiness problem, namely, Sybil attacks, in participatory sensing. A Sybil attack is defined as a malicious illegal presentation of multiple identities, called Sybil identities.These Sybil identities will intend to spread misinformation to reduce the effectiveness of sensing data in the participatory sensing network. To cope with this problem, a cloud based trust management scheme (CbTMS) framework was proposed to detect Sybil attacks in a participatory sensing network. The CbTMS was proffered for performing Sybil attack characteristic checks, in addition to a trustworthiness management system, to verify coverage nodes in participatory sensing. Simulation studies show that the proposed CbTMS can efficiently detect numerous defined malicious Sybil nodes with lower power consumption in the network.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙

Time- or Space-Dependent Coefficient Recovery in Parabolic Partial Differential Equation for Sensor Array in the Biological Computing

This study presents numerical schemes for solving a parabolic partial differential equation with a time- or space-dependent coefficient subject to an extra measurement. Through the extra measurement, the inverse problem is transformed into an equivalent nonlinear equation which is much simpler to handle. By the variational iteration method, we obtain the exact solution and the unknown coefficients. The results of numerical experiments and stable experiments imply that the variational iteration method is very suitable to solve these inverse problems

Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level

We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes

Time-and-ID-Based Proxy Reencryption Scheme

Time- and ID-based proxy reencryption scheme is proposed in this paper in which a type-based proxy reencryption enables the delegator to implement fine-grained policies with one key pair without any additional trust on the proxy. However, in some applications, the time within which the data was sampled or collected is very critical. In such applications, for example, healthcare and criminal investigations, the delegatee may be interested in only some of the messages with some types sampled within some time bound instead of the entire subset. Hence, in order to carter for such situations, in this paper, we propose a time-and-identity-based proxy reencryption scheme that takes into account the time within which the data was collected as a factor to consider when categorizing data in addition to its type. Our scheme is based on Boneh and Boyen identity-based scheme (BB-IBE) and Matsuo’s proxy reencryption scheme for identity-based encryption (IBE to IBE). We prove that our scheme is semantically secure in the standard model

A Method of Extracting Ontology Module Using Concept Relations for Sharing Knowledge in Mobile Cloud Computing Environment

In mobile cloud computing environment, the cooperation of distributed computing objects is one of the most important requirements for providing successful cloud services. To satisfy this requirement, all the members, who are employed in the cooperation group, need to share the knowledge for mutual understanding. Even if ontology can be the right tool for this goal, there are several issues to make a right ontology. As the cost and complexity of managing knowledge increase according to the scale of the knowledge, reducing the size of ontology is one of the critical issues. In this paper, we propose a method of extracting ontology module to increase the utility of knowledge. For the given signature, this method extracts the ontology module, which is semantically self-contained to fulfill the needs of the service, by considering the syntactic structure and semantic relation of concepts. By employing this module, instead of the original ontology, the cooperation of computing objects can be performed with less computing load and complexity. In particular, when multiple external ontologies need to be combined for more complex services, this method can be used to optimize the size of shared knowledge