Privacy preserving record linkage approaches

Vassilios S. Verykios, Alexandros Karakasidis and Vassilios K. Mitrogiannis

International Journal of Data Mining, Modelling and Management, 2009, vol. 1, issue 2, 206-221

Abstract: Privacy-preserving record linkage is a very important task, mostly because of the very sensitive nature of the personal data. The main focus in this task is to find a way to match records from among different organisation data sets or databases without revealing competitive or personal information to non-owners. Towards accomplishing this task, several methods and protocols have been proposed. In this work, we propose a certain methodology for preserving the privacy of various record linkage approaches and we implement, examine and compare four pairs of privacy preserving record linkage methods and protocols. Two of these protocols use n-gram based similarity comparison techniques, the third protocol uses the well known edit distance and the fourth one implements the Jaro-Winkler distance metric. All of the protocols used are enhanced by private key cryptography and hash encoding. This paper presents also a blocking scheme as an extension to the privacy preserving record linkage methodology. Our comparison is backed up by extended experimental evaluation that demonstrates the performance achieved by each of the proposed protocols.

Keywords: data integration; privacy preserving record linkage; cryptography; personal data; privacy protection; private key cryptography; hash encoding; blocking schemes. (search for similar items in EconPapers)
Date: 2009
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.inderscience.com/link.php?id=26076 (text/html)
Access to full text is restricted to subscribers.

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:ids:ijdmmm:v:1:y:2009:i:2:p:206-221

Access Statistics for this article

More articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().