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Filtering by: Committee Member Bobba, Rakesh Remove constraint Committee Member: Bobba, Rakesh Date 2016 Remove constraint Date: 2016 Rights Statement In Copyright Remove constraint Rights Statement: In Copyright

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  • Efficient Oblivious Access to Trees

    Creator:
    Hackebeil, Gabriel A.
    Abstract:
    The outsourcing of data storage and related infrastructure to third-party services in the cloud is a trend that has gained considerable momentum in the last decade due to the savings it affords companies in both capital and operational costs. Although encryption can alleviate some of the privacy concerns associated with...
    Resource Type:
    Masters Thesis

  • ScaleIO Storage Driver Based on CoprHD Southbound SDK

    Creator:
    Rajgopal, Varun
    Abstract:
    CoprHD is an open source software defined storage controller platform. It holds an inventory of all storage devices in the data center and understands their connectivity. It is an operating system for a storage cloud. It is designed with two key goals in mind: • Make an enterprise or a...
    Resource Type:
    Capstone Project

  • Enhancing Instructors’ Teaching Experience through Virtualization

    Creator:
    Patil, Ajinkya
    Abstract:
    In the current education environment, many instructors make use of some type of software, such as Visual Studio or a software library like OpenGL, in the classroom. Incorrect setup and configuration on an individual’s own system is a common problem when using these software tools. This thesis explores the difficulty...
    Resource Type:
    Masters Thesis

  • Jamming Attack Resiliency and Performance Analysis of Cognitive Radio Communication Networks

    Creator:
    Adem, Nadia
    Abstract:
    Cognitive radio technology emerges as a promising solution for overcoming shortage and inefficient use of spectrum resources. In cognitive radio networks, secondary users, which are users equipped with cognitive radios, can opportunistically access spectrum assigned to primary users, the spectrum license holders. Although it improves spectrum utilization efficiency, this opportunistic...
    Resource Type:
    Dissertation

  • Don't Fool Me : Detecting Adversarial Examples in Deep Networks

    Creator:
    Li, Xin
    Abstract:
    Deep learning has greatly improved visual recognition in recent years. However, recent research has shown that there exist many adversarial examples that can negatively impact the performance of such an architecture. Different from previous perspectives that focus on improving the classifiers to detect the adversarial examples, this work focuses on...
    Resource Type:
    Masters Thesis