Building an Anonymization Pipeline

Preface

A few years ago we partnered with O’Reilly to write a book of case studies and methods for anonymizing health data, walking readers through practical methods to produce anonymized data sets in a variety of contexts.¹ Since that time, interest in anonymization, sometimes also called de-identification, has increased due to the growth and use of data, evolving and stricter privacy laws, and expectations of trust by privacy regulators, by private industry, and by citizens from whom data is being collected and processed.

Why We Wrote This Book

The sharing of data for the purposes of data analysis and research can have many benefits. At the same time, concerns and controversies about data ownership and data privacy elicit significant debate. O’Reilly’s “Data Newsletter” on January 2, 2019, recognized that tools for secure and privacy-preserving analytics are a trend on the O’Reilly radar. Thus an idea was born: write a book that provides strategic opportunities to leverage the spectrum of identifiability to disassociate the personal from data in a variety of contexts to enhance privacy while providing useful data. The result is this book, in which we explore end-to-end solutions to reduce the identifiability of data. We draw on various data collection models and use cases that are enabled by real business needs, have been learned from working in some of the most demanding data environments, and are based on practical approaches that have stood the test of time.

The central ...