Department of Statistics Unitmark
Dietrich College of Humanities and Social Sciences

A Bayesian Approach to Data Disclosure: Optimal Intruder Behavior for Continuous Data

Publication Date

November, 1994

Publication Type

Tech Report

Author(s)

Stephen E. Fienberg, Udi E. Makov and Ashish P. Sanil

Abstract

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.