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Published Articles >> Table of Contents >> Abstract
21st International Conference on Data Engineering (ICDE'05)
pp. 205-216
Top-Down Specialization for Information and Privacy Preservation
Benjamin C. M. Fung, Simon Fraser University
Ke Wang, Simon Fraser University
Philip S. Yu, IBMT. J. Watson Research Center
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDE.2005.143
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| Abstract |
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Releasing person-specific data in its most specific state
poses a threat to individual privacy. This paper presents a
practical and efficient algorithm for determining a generalized
version of data that masks sensitive information and
remains useful for modelling classification. The generalization
of data is implemented by specializing or detailing the
level of information in a top-down manner until a minimum
privacy requirement is violated. This top-down specialization
is natural and efficient for handling both categorical
and continuous attributes. Our approach exploits the fact
that data usually contains redundant structures for
classification. While generalization may eliminate some structures,
other structures emerge to help. Our results show that
quality of classification can be preserved even for highly restrictive
privacy requirements. This work has great applicability
to both public and private sectors that share information
for mutual benefits and productivity.
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 Additional Information
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Citation:
Benjamin C. M. Fung, Ke Wang, Philip S. Yu,
"Top-Down Specialization for Information and Privacy Preservation,"
icde,
pp. 205-216,
21st International Conference on Data Engineering (ICDE'05),
2005
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