Blind attribute pairing for privacy-preserving record linkage
| Autor: | Thiago Pereira da Nóbrega, Carlos Eduardo Santos Pires, Tiago Brasileiro Araújo, Demetrio Gomes Mestre |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Information privacy Information retrieval Computer science Information sharing Database schema 02 engineering and technology computer.software_genre Schema matching 03 medical and health sciences 030104 developmental biology 020204 information systems Pairing Schema (psychology) 0202 electrical engineering electronic engineering information engineering computer Record linkage Data integration |
| Zdroj: | SAC |
| DOI: | 10.1145/3167132.3167193 |
| Popis: | In many scenarios, it is necessary to identify records referring to the same real-world object across different data sources (Record Linkage). Yet, such need is often in contrast with privacy requirements concerning (e.g., identify patients with the same diseases, genome matching, and fraud detection). Thus, in the cases where the parties interested in the Record Linkage process need to preserve the privacy of their data, Privacy-Preserving Record Linkage (PPRL) approaches are applied to address the privacy problem. In this sense, the first step of PPRL is the agreement of the parties about the data (attributes) that will be used during the record linkage process. Thus, to reach an agreement, the parties must share information about their data schema, which in turn can be utilized to break the data privacy. To overcome the (vulnerability) problem caused by the schema information sharing, we propose a novel privacy-preserving approach for attribute pairing to aid PPRL applications. Empirical experiments demonstrate that our privacy-preserving approach improves considerably the efficiency and effectiveness in comparison to a state-of-the-art baseline. |
| Databáze: | OpenAIRE |
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