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For DevOps, data needs to be made available to users in order to do bug fixes and software improvements. However, when that data is sensitive, it is risky to make that available to a broad group of users who could either be internal or third parties.


How can technology reduce the risk of using sensitive data for DevOps?




Publisher can de-identify sensitive data and make it useful for DevOps scenarios. Our privacy algorithms can preserve the richness and referential integrity of the dataset. This means de-identified data has higher quality than data from synthetic generators.

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Key Features

Publisher for DevOps

  • De-identify sensitive data with masking, perturbation or generalisation techniques
  • Preservation of useful patterns in data and retention referential integrity maintains more richness of de-identified data compared to synthetically generated data
  • Embed watermarks in datasets to facilitate auditing and data lineage
  • Automate privacy policy creation and centrally manage policies consistently across datasets in the enterprise
  • Retain customer data format, e.g. email addresses can be represented in email form


Data Privacy for DevOps Brief

Learn how you can quickly and securely provision valuable data to DevOps environments

Download Solution Brief


Why you really don't need production data for Test and Dev

We’re living in the age of big data, but without a way to securely provision to Analytics, Machine Learning and Test and Dev environments, many organisations still aren’t feeling a big difference..

Read Blog Post


The 6 principles of privacy-safe Test and Dev data

Download here our checklist. It lays out six principles to follow to ensure your data is both safe and useful for Test and Dev purposes.

Download Checklist

Watch Privitar’s Kieron Guinamard talk about the key issues around Facebook and Cambridge Analytica.

Watch the Video

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