Pseudonymization is a data processing technique that preserves consumer privacy by obscuring personally identifiable information (PII). While anonymizing processes do this by removing the relevant attribute values (or identifiers) from fields such as name, address, or phone number, pseudonymization replaces the relevant attribute value with stand-in or alias values, or pseudonyms.
Why would someone use pseudonymization?
Pseudonymization is a technique intended to preserve privacy and data confidentiality. It is a global practice, noted in both the GDPR and CCPA data regulatory standards. For example, Recital 29 of the GDPR states that the GDPR aims “to create incentives to apply pseudonymization when processing personal data.”
Pseudonymization’s advantage over strict anonymization techniques is twofold: 1) pseudonymization preserves the ability for a data owner to securely reidentify an individual data record under specific managed circumstances, such as when it’s required for regulatory reporting or notification, and 2) the creation of consistent pseudonyms over related records enables those records to be linked, preserving the ability of data to be augmented or enriched.
Think of the data owner as a chef with countless recipes in her head and dishes that she alone can make. If we consider her culinary repertoire as data, pseudonymization allows her to strip out secret ingredients but still logically group her entrees, desserts, and appetizers together and enhance them with new flavors. Strict anonymization would only allow the former.
When would data collaborators choose to use pseudonymization?
Beyond preserving consumer privacy, pseudonymization is also a technique enabling marketers to securely collaborate for analytic and measurement purposes at both the aggregate and data record level. For example, pseudonymized records can still be linked across multiple partner data tables to produce insights about a path to purchase or the true incremental impact of a campaign. Pseudonymized records can also be used to create rich audience behavioral models to guide marketing planning and tactics.
To learn more about the power of data collaboration, read this blog.