Date: Wednesday 11th December 2013
Time: 16:00-17:00 (tea/coffee and mince pies available from 15.30)
Venue: Room G207, Alan Turing Building, The University of Manchester, Manchester
Building 46 on the campus map: http://www.manchester.ac.uk/aboutus/travel/maps/interactive-map/
Assessing Disclosure Risk in Sample Microdata
Professor Natalie Shlomo
Professor of Social Statistics, The Cathie Marsh Centre for Census and Survey Research, University of Manchester
Disclosure risk occurs when there is a high probability that an intruder can identify an individual in released sample microdata and confidential information may be revealed. For most social surveys, the population from which the sample is drawn is generally not known or only partially known through marginal distributions. The identification is made possible through the use of a key, which is a combination of indirectly identifying variables, such as age, sex and place of residence. Disclosure risk measures are based on the notion of population uniqueness in the key. In order to quantify the disclosure risk, probabilistic models are defined based on distributional assumptions about the population counts inferred from the observed sample counts. The parameters for the distribution are estimated through log-linear models. The probabilistic framework is expanded to cover the case of misclassification on the key variables, either arising from the survey process or as a result of perturbative disclosure control techniques that may have been applied to the data. The methods are demonstrated on real data drawn from extracts of the 2001 United Kingdom Census.
This is joint work with Prof. Chris Skinner of the London School of Economics and Political Science.