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tkl-Score: A Misuseability Score for Deciding How To Share Data
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- Author / Creator
- Eng, Kalvin
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As more and more data is collected, individuals and organizations are beginning to share their collected data to gain valuable insights. In doing so, these data stakeholders must be aware of the kind of impact that releasing data will have. Therefore, the misuseability scores M-Score and L-Severity have been developed to provide a measure of the potential damage to individuals and organizations when sensitive information from a dataset is released. This thesis introduces tkl-Score and its derivative tkl-Score{max} which augments M-Score and L-Severity measures by increasing record scores when records are more identifiable in a source table with l-Distinguishing Factor, and also increasing record scores when sensitive attributes are less granular in a source table with l-Distinguishing Factor and t-Distinguishing Factor. In contrast, M-Score and L-Severity account for only record identifiability in a source table with k-Distinguishing Factor. tkl-Score and tkl-Score{max} are shown to better characterize the risk of releasing records compared to M-Score and L-Severity due to accounting for sensitive attribute granularity.
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- Subjects / Keywords
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- Graduation date
- Fall 2020
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- Type of Item
- Thesis
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- Degree
- Master of Science
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- License
- Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.