How can you use data in a way that protects individual privacy but still provides useful and meaningful analytics? With this practical book, data architects and engineers will learn how to establish and integrate secure, repeatable anonymization processes into their data flows and analytics in a sustainable manner.
Luk Arbuckle and Khaled El Emam from Privacy Analytics explore end-to-end solutions for anonymizing device and IoT data, based on collection models and use cases that address real business needs. These examples come from some of the most demanding data environments, such as healthcare, using approaches that have withstood the test of time.
- Create anonymization solutions diverse enough to cover a spectrum of use cases
- Match your solutions to the data you use, the people you share it with, and your analysis goals
- Build anonymization pipelines around various data collection models to cover different business needs
- Generate an anonymized version of original data or use an analytics platform to generate anonymized outputs
- Examine the ethical issues around the use of anonymized data
Table of Contents
- 1. Introduction
- 2. Identifiability Spectrum
- 3. A Practical Risk-Management Framework
- 4. Identified Data
- 5. Pseudonymized Data
- 6. Anonymized Data
- 7. Safe Use
- Title: Building an Anonymization Pipeline
- Release date: April 2020
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781492053439