Optimization Methods for Tabular Data Protection

In this thesis we consider a minimum distance Controlled Tabular Adjustment (CTA) model for statistical disclosure limitation (control) of tabular data. The goal of the CTA model is to find the closest safe table to some original tabular data set that contains sensitive information. The measure of closeness is usually measured using l1 or l2 norm; with each measure having its advantages and disadvantages. According to the given norm CTA can be formulated as an optimization problem: Liner Programing (LP) for l1, Quadratic Programing (QP) for l2. In this thesis we present an alternative reformulation of l1-CTA as Second-Order Cone (SOC) optimization problems. All three models can be solved using appropriate versions of Interior-Point Methods (IPM). The validity of the new approach was tested on the randomly generated two-dimensional tabular data sets. It was shown numerically, that SOC formulation compares favorably to QP and LP formulations