De-Identification

De-Identification Requires a Platform Approach

The scope of data, users and purposes in your enterprise is vast. You need a data privacy platform that can meet the requirements for every context; a simple one size fits all using only data masking or data tokenization will fall flat.

That’s why the Privitar Data Privacy Platform™ enables you to select from the full range of de-identification techniques in any combination so you can tune the data resolution and re-identification risk according to each individual analysis, model or use.

Access Privitar’s Guide to De-Identification

The Privitar De-identification 101 series provides a ground-level introduction to the issues surrounding de-identification, one of the fundamental components of data privacy.

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Understand Data Context

A successful data privacy program takes full account of data context. Context includes:

Dataset required for each analysis/model
Combinations of direct and quasi-identifiers
Different access rights and privileges of the employees and third parties
Data users’ access to other datasets that could be used in a linkage attack
The questions asked and insights sought

This context determines how you will combine de-identification techniques in a Protected Data Domain.™