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


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.