Afficher la notice abrégée
dc.creator | Karakasidis, A. | en |
dc.creator | Verykios, V. S. | en |
dc.date.accessioned | 2015-11-23T10:33:01Z | |
dc.date.available | 2015-11-23T10:33:01Z | |
dc.date.issued | 2012 | |
dc.identifier | 10.1145/2245276.2245444 | |
dc.identifier.isbn | 9781450308571 | |
dc.identifier.uri | http://hdl.handle.net/11615/28964 | |
dc.description.abstract | Privacy Preserving Record Linkage is an emerging field of research which attempts to deal with the classical linkage problem from a privacy preserving point of view. In this paper we propose a novel approach for performing Privacy Preserving Blocking in order to minimize the computational cost of Privacy Preserving Record Linkage. We achieve this without compromising privacy by using Nearest Neighbors clustering, a well-known clustering algorithm and by using a reference table. A reference table is a publicly known table the contents of which are used as intermediate references. The combination of Nearest Neighbors and a reference table offers our approach k-anonymity characteristics. © 2012 ACM. | en |
dc.source.uri | http://www.scopus.com/inward/record.url?eid=2-s2.0-84863594425&partnerID=40&md5=9063c57364258786a10058252c6c05b7 | |
dc.subject | clustering | en |
dc.subject | k-anonymity | en |
dc.subject | privacy | en |
dc.subject | Privacy Preserving Record Linkage | en |
dc.subject | private blocking | en |
dc.subject | reference tables | en |
dc.subject | Record linkage | en |
dc.subject | Clustering algorithms | en |
dc.subject | Data privacy | en |
dc.title | Reference table based k-anonymous private blocking | en |
dc.type | conferenceItem | en |
Fichier(s) constituant ce document
Il n'y a pas de fichiers associés à ce document.
|
Ce document figure dans la(les) collection(s) suivante(s)
Afficher la notice abrégée