Here - Tilburg University

Presenter
Schnell, Rainer; Institut für Soziologie, University of Duisburg-Essen
Authors
Rainer Schnell; Tobias Bachteler and J. Reiher; University of Duisburg-Essen
Title
Bloom filter based cryptographic personal identification keys for longitudinal
research.
Abstract
Longitudinal micro data are a rich source of information on important
research topics all through the social sciences. To obtain longitudinal data
individuals must however be tracked over time. For example, in epidemiological
research, a national cohort may be tracked life-long in databases of health care
providers. In criminological research, the identity of offend-ers has to be known
for computing individual risk of recidivism.
If no unique national personal identification numbers are available, the
linkage of person-al data of the same individual across time is usually based on
pseudonyms. Since this raises privacy concerns, methods of privacy preserving
identity management in longitudinal re-search are needed.
So far, quite simple algorithms for the generation of pseudonyms based
on personal cha-racteristics (names, date and place of birth) are in common use.
However, these algorithms will yield non matching pseudonyms when errors or
changes in the underlying information occur.
In Schnell et al. (2009) we suggested to use Bloom filters for calculating
string similarities in a privacy-preserving manner. Here, we claim that this
principle can also be used for a cryptographic long-term stable key (CLK) that
provides both privacy and fault-tolerance. Us-ing simulated data we evaluate its
practicability and compare it to previously proposed al-ternative methods.
References:
Schnell, R., Bachteler, T. & Reiher, J. (2009): Privacy-preserving record linkage
using Bloom filters; in: BMC Medical Informatics and Decision Making 9 (41).