Improving record linkage with supervised learning for disclosure risk assessment
| Autor: | Daniel Abril, Guillermo Navarro-Arribas, Vicenç Torra |
|---|---|
| Přispěvatelé: | Consejo Superior de Investigaciones Científicas (España), Ministerio de Ciencia e Innovación (España) |
| Rok vydání: | 2012 |
| Předmět: |
Linkage (software)
Information privacy Computer science Supervised learning Estimator Worst-case scenario 02 engineering and technology 16. Peace & justice computer.software_genre 01 natural sciences 010104 statistics & probability Record linkage Hardware and Architecture Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining 0101 mathematics Risk assessment computer Weighted arithmetic mean Data privacy Software Information Systems |
| Zdroj: | Digital.CSIC. Repositorio Institucional del CSIC instname Universitat Autònoma de Barcelona |
| Popis: | In data privacy, record linkage can be used as an estimator of the disclosure risk of protected data. To model the worst case scenario one normally attempts to link records from the original data to the protected data. In this paper we introduce a parametrization of record linkage in terms of a weighted mean and its weights, and provide a supervised learning method to determine the optimum weights for the linkage process. That is, the parameters yielding a maximal record linkage between the protected and original data. We compare our method to standard record linkage with data from several protection methods widely used in statistical disclosure control, and study the results taking into account the performance in the linkage process, and its computational effort. © 2011 Elsevier B.V. All rights reserved. Partial support by the Spanish MICINN (projects TSI2007-65406-C03-02, TIN2010-15764, ARES-CONSOLIDER INGENIO 2010 CSD2007-00004), and European Commission (project Data without Boundaries (DwB), Grant Agreement Number 262608) is acknowledged. |
| Databáze: | OpenAIRE |
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