Data Tokenization

Control consistency, format preservation and reversibility

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Data Tokenization With the Privitar Data Privacy Platform™

Data tokenization is the process used to de-identify direct identifiers by replacing raw data with randomly generated tokens, or pseudonyms. The Privitar Data Privacy Platform™ supports tokenization with optional consistency, format preservation and reversibility.

Enable Consistency & Linkability

Within a dataset or analysis, Privitar Protected Data Domains™ enable consistent data tokenization so you can maintain referential integrity and enrich data where needed.

Consistency is maintained over time so that as new data becomes available or additional data is required, you can add it to tokenized data in a Protected Data Domain.

Preserve Format

Privitar data tokenization supports format and pattern preservation using a range of methods. You decide which format preservation techniques to use:

  • Full control for structured formats, such as phone numbers and postal codes
  • Lookup lists, such as first and last names
  • Generators for numbers, dates and credit cards

Re-Identify Tokenized Data

Whether it’s for a healthcare intervention, a personalized customer experience or suspected fraud; de-tokenization can be critical.

Privitar lets you decide when tokenized data can be reversed. De-tokenization is a privileged role in Privitar, so you can ensure proper oversight and governance over who can see personally identifying data.

Access Privitar’s Guide to Data De-Identification

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

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Data Tokenization Built for Enterprise Standards

Consistency and reversibility are crucial to data tokenization. They require a Token Vault to securely store and map the original data values to the tokens that replace them. Privitar supports a range of Token Vault technologies to meet your enterprise standards.


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