Parallelizing Record Linkage for Disclosure Risk Assessment

Guisado-Gámez, Joan; Prat-Pérez, Arnau; Nin, Jordi; Muntés-Mulero, Víctor; Larriba-Pey, Josep Lluís

doi:10.1007/978-3-540-87471-3_16

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Then, according to Cohen et al (2003), records are assigned to blocks using a token-based blocking method. Another approach to improve efficiency is proposed by Guisado-Gámez et al (2008) who perform record linkage in a parallel environment. Another approach to improve efficiency is proposed by Guisado-Gámez et al (2008) who perform record linkage in a parallel environment.…”

Section: Related Workmentioning

confidence: 99%

Privacy preserving record linkage approaches

Verykios

Karakasidis

Mitrogiannis

2009

IJDMMM

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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. Privacy preserving record linkage approaches 207His main research interests include privacy and security in advanced database systems, data mining, and data reconciliation. He has published over 60 papers in major refereed journals and international conferences and workshops and he has served in the program committees of several international scientific events.

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Section: Related Workmentioning

confidence: 99%

Privacy preserving record linkage approaches

Verykios

Karakasidis

Mitrogiannis

2009

IJDMMM

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“…The computational and communication overheads of most approaches developed so far prohibit the matching of large databases that contain many millions of records. What is required are novel techniques that scale linearly with the number of records to be matched, and also techniques that take advantage of the parallel multi-core computing capabilities that increasingly become available on many modern computing platforms [47]. A related challenge is the capability to match very large databases with a stream of incoming query records in (near) real-time [48], because real-time matching in a privacy-preserving framework is becoming increasingly important.…”

Section: -Scalability To Large Databases and Real-time Matchingmentioning

confidence: 99%

Geocode Matching and Privacy Preservation

Christen

2009

Privacy, Security, and Trust in KDD

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Abstract. Geocoding is the process of matching addresses to geographic locations, such as latitudes and longitudes, or local census areas. In many applications, addresses are the key to geo-spatial data analysis and mining. Privacy and confidentiality are of paramount importance when data from, for example, cancer registries or crime databases is geocoded. Various approaches to privacy-preserving data matching, also called record linkage or entity resolution, have been developed in recent times. However, most of these approaches have not considered the specific privacy issues involved in geocode matching. This paper provides a brief introduction to privacy-preserving data and geocode matching, and using several real-world scenarios the issues involved in privacy and confidentiality for data and geocode matching are illustrated. The challenges of making privacy-preserving matching practical for real-world applications are highlighted, and potential directions for future research are discussed.

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Privacy and anonymization for very large datasets

Muntés-Mulero

Nin²

2009

Proceedings of the 18th ACM Conference on Information and Knowledge Management

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Parallelizing Record Linkage for Disclosure Risk Assessment

Cited by 3 publications

References 10 publications

Privacy preserving record linkage approaches

Privacy preserving record linkage approaches

Geocode Matching and Privacy Preservation

Privacy and anonymization for very large datasets

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