A Bayesian Approach to Data Disclosure: Optimal Intruder Behavior for Continuous Data
Stephen E. Fienberg, Udi E. Makov and Ashish P. Sanil
In this paper we develop a Bayesian approach to data
disclosure in survey settings by adopting a probabilistic definition
of disclosure due to Dalenius and the principle that a data collection
agency must consider disclosure from the perspective of an intruder in
order to efficiently evaluate data disclosure limitation procedures.
Our approach leads to a formal model involving mixture distributions.
We then discuss the implementation of a simplified version of the
model which is made computationally feasible by the use of
Gibbs sampling in conjunction with several approximations. We apply
the methods in a small-scale simulation study using data extracted and
adapted from an actual survey conducted by the Institute for Social
Research at York University.
Keywords: Confidentiality; Disclosure limitation; Gibbs sampling;
Inferential disclosure; Measurement error.
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