Anonymizing Health Data

Chapter 1. Introduction

Anonymization, sometimes also called de-identification, is a critical piece of the healthcare puzzle: it permits the sharing of data for secondary purposes. The objective of this book is to walk you through practical methods to produce anonymized data sets in a variety of contexts. This isn’t, however, a book of equations—you can find those in the references we provide. We hope to have a conversation, of sorts, to help you understand some of the problems with anonymization and their solutions.

Because the techniques used to achieve anonymization can’t be separated from their context—the exact data you’re working with, the people you’re sharing it with, and the goals of analysis—this is partly a book of case studies. We include many examples to illustrate the anonymization methods we describe. Each case study was selected to highlight a specific technique, or to explain how to deal with a certain type of data set. They’re based on our experiences anonymizing hundreds of data sets, and they’re intended to provide you with a broad coverage of the area.

We make no attempt to review all methods that have been invented or proposed in the literature. We focus on methods that have been used extensively in practice, where we have evidence that they work well and have become accepted as reasonable things to do. We also focus on methods that we’ve used because, quite frankly, we know them well. And we try to have a bit of fun at the same time, with plays on words and ...