De-identification

De-identification is the process of modifying data to reduce or remove identifiers that could reasonably be used to identify an individual. Techniques such as masking, tokenization, generalization, aggregation, suppression, and pseudonymization may be used individually or in combination to reduce the likelihood of identification. The appropriate approach depends on the intended use of the data, the level of privacy protection required, and the risk of re-identification.

Organizations use de-identified data to support analytics, research, testing, product development, artificial intelligence, and operational reporting while reducing exposure of directly identifiable personal information. However, de-identification is not a one-time activity. Organizations should continuously assess re-identification risks, particularly when multiple datasets can be linked together or when additional contextual information becomes available.

The Digital Personal Data Protection Act, 2023 defines personal data as data about an individual who is identifiable by or in relation to such data. Whether de-identified information falls outside the scope of the Act depends on whether an individual remains identifiable, directly or indirectly, in relation to that data. Organizations should therefore evaluate de-identification techniques carefully and avoid assuming that simply removing obvious identifiers automatically takes data outside the scope of the Act.

In practice, gaps emerge when:

• Direct identifiers are removed, but individuals remain identifiable through other data elements.
• Different datasets can be combined to re-identify individuals.
• De-identification methods are applied inconsistently across business units.
• Organizations do not periodically evaluate re-identification risks.
• Teams use production personal data for testing when lower-risk alternatives are available.

Organizations reduce these risks by implementing documented de-identification policies, selecting techniques appropriate to the intended use of the data, assessing re-identification risks, and continuously reviewing governance controls. Within Privy, capabilities such as data discovery, classification, governance workflows, and privacy assessments help organizations identify personal data and strengthen privacy governance around de-identification practices.