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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).

Record type: Monograph (Working Paper)


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'.

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Published date: 18 October 2010
Keywords: identification disclosure, attribute disclosure, differential privacy, misclassification


Local EPrints ID: 165757
PURE UUID: 62f571c7-8c09-4bc8-80aa-c844fc755d73

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Date deposited: 19 Oct 2010 09:08
Last modified: 18 Jul 2017 12:26

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Author: Natalie Shlomo
Author: Chris Skinner

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