Privacy Protection from Sampling and Perturbation in Survey Microdata


Shlomo, Natalie and Skinner, Chris (2010) Privacy Protection from Sampling and Perturbation in Survey Microdata. Southampton, GB, Southampton Statistical Sciences Research Institute, 15pp. (S3RI Methodology Working Papers, M10/14).

Download

[img] PDF
Download (251Kb)

Description/Abstract

We consider the assessment of disclosure risk in the release of microdata from social surveys as public-use files. We consider both identification risk and the notion of differential privacy introduced in the computer science literature. We show that sampling, as a disclosure limitation technique, does not guarantee differential privacy. However, threats to differential privacy, i.e. 'leakage', may have small probability and sampling can provide protection under a broader definition of privacy. Moreover, the occurrence of conditions when such a threat can occur may be unknown to the adversary and require statistical inference. Disclosure limitation techniques that perturb variables in the microdata according to misclassification probabilities guarantee differential privacy provided that there are no zero elements in the misclassification mechanism. Combining sampling and perturbation, especially for rare combinations of identifying variables, will reduce the 'leakage'.

Item Type: Monograph (Working Paper)
Keywords: identification disclosure, attribute disclosure, differential privacy, misclassification
Subjects: H Social Sciences > HA Statistics
Divisions: University Structure - Pre August 2011 > Southampton Statistical Sciences Research Institute
ePrint ID: 165757
Date Deposited: 19 Oct 2010 09:08
Last Modified: 27 Mar 2014 19:18
Publisher: Southampton Statistical Sciences Research Institute
URI: http://eprints.soton.ac.uk/id/eprint/165757

Actions (login required)

View Item View Item