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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.)

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.

Suggested Citation

  • Akimichi Takemura, 1999. "Local Recoding by Maximum Weight Matching for Disclosure Control of Microdata Sets," CIRJE F-Series CIRJE-F-40, CIRJE, Faculty of Economics, University of Tokyo.
    • Handle: RePEc:tky:fseres:99cf40
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