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Obtaining information while preserving privacy: A Markov perturbation method for tabular data (1997)
| Venue: | IN JOINT STATISTICAL MEETINGS |
| Citations: | 39 - 11 self |
BibTeX
@INPROCEEDINGS{Duncan97obtaininginformation,
author = {George T. Duncan and Stephen E. Fienberg},
title = {Obtaining information while preserving privacy: A Markov perturbation method for tabular data},
booktitle = {IN JOINT STATISTICAL MEETINGS},
year = {1997},
publisher = {}
}
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Abstract
Preserving privacy appears to conflict with providing information. Statistical information can, however, be provided while preserving a specified level of confidentiality protection. The general approach is to provide disclosure-limited data that maximizes its statistical utility subject to confidentiality constraints. Disclosure limitation based on Markov chain methods that respect the underlying uncertainty in real data is examined. For use with categorical data tables a method called Markov perturbation is proposed as an extension of the PRAM method of Kooiman, Willenborg, and Gouweleeuw (1997). Markov perturbation allows cross-classified marginal totals to be maintained and promises to provide more information than the commonly used cell suppression technique.
Keyphrases
tabular data markov perturbation method markov perturbation disclosure-limited data underlying uncertainty statistical utility subject markov chain method confidentiality protection specified level real data general approach disclosure limitation statistical information cell suppression technique categorical data table confidentiality constraint pram method cross-classified marginal total
