Building an Anonymization Pipeline by Luk Arbuckle

Author:Luk Arbuckle
Language: eng
Format: epub
Publisher: O'Reilly Media
Published: 2020-04-10T00:00:00+00:00

Dealing with Indirect Identifiers

Ridding yourself of indirect identifiers, in the same fashion as direct identifiers, would mean eliminating all risk (sounds good!) as well as all analytic utility from data rendered anonymous (oh my, that’s terrible!). We’ve described the methods of measuring identifiability in a previous book.3 And we’ve walked you through the basic concepts of measuring identifiability in Chapter 2. No matter which technological approach we use, these concepts will apply.

Rather than removing the indirect identifiers, we will transform the data/outputs to ensure the level of identifiability achieves a defensible threshold used to provide reasonable assurance that data is nonidentifiable. But we’ve already provided a framework for doing this in Chapter 3.

The Five Safes, operationalized through risk-based anonymization, are both a governance framework and the basis for evaluating identifiability in the context of sharing data. That’s because changes to any one of the Safes can change our assessment of identifiability. They are intimately linked! Consider all the factors that affect the data-sharing context, shown in Figure 4-3.

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