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Implementing A Nonlinear Optimization Procedure to Estimate Disclosure Risk

Betsy S. Greenberg
Component ID: #ti853820827

Introduction

Disclosure risk is usually assessed by estimating the number of individuals in a population with unique characteristics. The problem is difficult because there are situations for which samples from two or more very different populations can be nearly identical. This project will examine an optimization procedure to estimate the number of unique individuals in a population based on sample information. The resulting optimization program has multiple solutions corresponding to different populations that could have been the source of the sample data. Many if not all local solutions can be found using a new global optimization algorithm called OptQuest NLP (OQNLP). The goal of this project was to modify the program to work on large practical problems, test the procedure with public use Census data, and describe the result and its limitations.

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