Optimality models for PRAM
Leon Willenborg · COMPSTAT · 2000
The paper deals with the application of a technique to protect microdata against disclosure. This technique is called the Post Randomization Method (PRAM), and it works through a random perturbation of categorical variables, identifiers in fact, in a microdata set. In this paper two optimization models are formulated to arrive at nonsingular PRAM matrices, which are stochastic (Markov) matrices. The models presented are Nonlinear Programming (NP) models. The first model turns out to be equivalent to a Linear Programming (LP) problem. The second model, which is close to a geometric programming model, can be reformulated as a nonlinear optimization problem under linear constraints.