The many advantages of cloud computing are increasingly attracting individuals and organizations to outsource their data from local to remote cloud servers. However, security and privacy concerns have arisen as obstacles to widespread adoption of clouds by users. While much cloud security research focuses on enforcing standard access control policies typical of centralized systems, such […]
Detecting and preventing a selfishness in Mobile Ad Hoc Network
A mobile ad-hoc network (MANET) is the collection of mobile nodes that are equipped by several wireless mobile devices which are using for communication. In MANET, most of the Replica allocation techniques are assuming that all mobile nodes cooperate fully in the network functionalities. Some nodes decide not to cooperate partially or fully. Network performance […]
Latency Equalization using greedy hub selection
In this paper, we design and implement network-based Latency EQualization (LEQ), which is a service that Internet service providers (ISPs) can provide for various interactive network applications. Our LEQ architecture provides a flexible routing framework that enables the network provider to implement different delay and delay difference optimization policies in order to meet the requirements […]
Selfish node detection in MANET
In this paper, we address the problem of selfishness in the context of replica allocation in a MANET, i.e., a selfish node may not share its own memory space to store replica for the benefit of other nodes. In this paper, we shall refer to such a problem as the selfish replica allocation. Simply, selfish […]
Co-distribution patterns Mining for Maximum crime datasets
Crime activities are geospatial phenomena and as such are geospatially, thematically and temporally correlated. We analyze crime datasets in conjunction with socio-economic and socio-demographic factors to discover co-distribution patterns that may contribute to the formulation of crime. We propose a graph based dataset representation that allows us to extract patterns from heterogeneous areal aggregated datasets […]
Class-dependent density-based feature Elimination
High-dimensional data sets are inherently sparse and hence, can be transformed to lower dimensions without losing too much information about the classes. In this paper we propose a new feature ranking algorithm, termed as, class-dependent density-based feature elimination, for binary data sets. CDFE uses a measure termed as, diff-criterion, to estimate the relevance of features. […]
Improving the classification performance
This paper proposes a new attribute construction approach which converts the original data attributes into a higher dimensional feature space to extract more attribute information by a similarity-based algorithm using the classification-oriented fuzzy membership function. Seven data sets with different attribute sizes are employed to examine the performance of the proposed method. The results show […]
Exploiting Excess Capacity to Improve Robustness of WDM Mesh Networks
Excess capacity (EC) is the unused capacity in a network. Excess capacity management techniques exploit the EC to improve the network performance. In this paper we propose EC management techniques that exploit EC to improve all the three performance metrics. EC management techniques differ in two respects: when connections are migrated from one protection scheme […]
CIA Framework for Data Sharing in Cloud
In this paper we propose a novel approach, namely Cloud Information Accountability (CIA) framework, based on the notion of information accountability. Our proposed CIA framework provides end-to-end accountability in a highly distributed fashion. One of the main innovative features of the CIA framework lies in its ability of maintaining lightweight and powerful accountability that combines […]
MLT-PPDM: Multi-Level Trust in Privacy Preserving Data Mining
In this paper, we address this challenge in enabling MLT-PPDM services. In particular, we focus on the additive perturbation approach where random Gaussian noise is added to the original data with arbitrary distribution, and provide a systematic solution. Through a one-to-one mapping, our solution allows a data owner to generate distinctly perturbed copies of its […]
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