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Local Recoding by Maximum Weight Matching for Disclosure Control of Microdata Sets

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  • Akimichi Takemura

    (Faculty of Economics, University of Tokyo.)

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    Abstract

    We propose "local recoding" as a new technique for controlling disclosure risk of microdata sets. Compared to the technique of global recoding, where the observed values are grouped into broader intervals or categories throughout the data set, in local recoding different grouping is performed for each observation when necessary. As a means of performing local recoding we propose to form pairs of close individuals and recode observed values within each pair. For optimally forming pairs we can employ Edmonds' algorithm (Edmonds(1965)) of maximum weight matching. We illustrate the technique by applying it to the Japanese vital statistics data.

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    File URL: http://www.cirje.e.u-tokyo.ac.jp/research/dp/99/cf40/contents.htm
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    Bibliographic Info

    Paper provided by CIRJE, Faculty of Economics, University of Tokyo in its series CIRJE F-Series with number CIRJE-F-40.

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    Length: 14 pages
    Date of creation: Feb 1999
    Date of revision:
    Handle: RePEc:tky:fseres:99cf40

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