Building an Anonymization Pipeline: Creating safe data

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