Zhang, Da Yong (2009) Modeling and evaluating information leakage caused by inferences in supply chains. Masters thesis, Concordia University.
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Abstract
While information sharing can benefit supply chains significantly, it may also have a "side effect", namely, information leakage. A limitation common to many existing solutions for preventing information leakage in supply chains is that they rely, either implicitly or explicitly, upon two unrealistic assumptions. First, what information is confidential is well known. Second, confidential information will not be revealed, if only it is not shared, regardless of how much other information is being shared. As it shall be shown in this thesis, those assumptions are not always true due to potential information leakage caused by inferences. Specifically, a conceptual model of such information leakage is proposed in this thesis. The model will enable companies in a supply chain to better understand how their confidential information may be leaked through inferences. On the basis of the proposed conceptual model, a quantitative approach is devised to evaluate the risk of information leakage caused by inferences when a given amount of information is shared. The quantitative approach will allow companies in a supply chain to measure and consequently to mitigate the risk of information leakage. Finally, a case study is discussed to illustrate how the proposed approaches work in practice.
Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering |
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Item Type: | Thesis (Masters) |
Authors: | Zhang, Da Yong |
Pagination: | xi, 76 leaves : ill. ; 29 cm. |
Institution: | Concordia University |
Degree Name: | M.A. Sc |
Program: | Institute for Information Systems Engineering |
Date: | 2009 |
Thesis Supervisor(s): | Wang, L |
Identification Number: | LE 3 C66I54M 2010 Z43 |
ID Code: | 976690 |
Deposited By: | Concordia University Library |
Deposited On: | 22 Jan 2013 16:31 |
Last Modified: | 13 Jul 2020 20:10 |
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