What is Data Anonymization?

Data anonymization is the process of mitigating direct and indirect privacy risks within data, such that there is a measurable way to ensure records cannot be attributed to a specific individual or entity. With an estimated 2.5 quintillion bytes of data being generated every day and an increasing reliance on data to power new applications, machine learning models and AI technologies, the importance of implementing effective anonymization techniques and removing any bottlenecks is crucial to accelerating future developments and innovations. This post is a general introduction to anonymization, and the tools and techniques for providing sufficient privacy protections, so that personally identifiable information (PII) is safe from exposure and exploitation. Data anonymization should be considered a continuous process; one that can require rapid iteration of applying various privacy engineering techniques and then measuring those privacy outcomes until a desired end state is reached. In the following sections, we'll dive deeper into our core tenets of the data anonymization process, and then walkthrough how you might apply them to a notional dataset.