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

Akimichi Takemura
Additional contact information
Akimichi Takemura: Faculty of Economics, University of Tokyo.

No CIRJE-F-40, CIRJE F-Series from CIRJE, 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.

Pages: 14 pages
Date: 1999-02
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.cirje.e.u-tokyo.ac.jp/research/dp/99/cf40/contents.htm (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tky:fseres:99cf40

Access Statistics for this paper

More papers in CIRJE F-Series from CIRJE, Faculty of Economics, University of Tokyo Contact information at EDIRC.
Bibliographic data for series maintained by CIRJE administrative office ().