Description

Corporations invest considerable resources to create, preserve and analyze

their data; yet while organizations are interested in protecting against

unauthorized data transfer, there lacks a comprehensive metric to discriminate

what data are at

Corporations invest considerable resources to create, preserve and analyze

their data; yet while organizations are interested in protecting against

unauthorized data transfer, there lacks a comprehensive metric to discriminate

what data are at risk of leaking.

This thesis motivates the need for a quantitative leakage risk metric, and

provides a risk assessment system, called Whispers, for computing it. Using

unsupervised machine learning techniques, Whispers uncovers themes in an

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    Contributors
    Date Created
    • 2014
    Resource Type
  • Text
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    Note
    • Partial requirement for: M.S., Arizona State University, 2014
      Note type
      thesis
    • Includes bibliographical references (p. 43-46)
      Note type
      bibliography
    • Field of study: Computer science

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    Statement of Responsibility

    by Jeremy Wright

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