Presented by:
Harvey Goldstein
Date:
Friday 8th July 2016 - 10:00 to 11:00
Venue:
INI Seminar Room 1
Abstract:
The general idea is to use
the addition of random noise with known properties to some or all variables in
a released dataset, typically following linkage, where the values of some
identifier variables for individuals of interest are also available to an
external ‘attacker’ who wishes to identify those individuals so that they can
interrogate their records in the dataset. The noise is tuned to achieve any
given degree of anonymity to avoid identification by an ‘attacker’ via the
linking of patterns based on the values of such variables. The noise so generated can then be ‘removed’
at the analysis stage since its characteristics are known, requiring disclosure
of these characteristics by the linking agency. This leads to consistent
parameter estimates, although a loss of efficiency will occur, but the data
themselves are not degraded by any form of coarsening such as grouping.