Blog

The Privacy Dynamics web UI is an easy and intuitive way to get started de-identifying your data, but now you can also use our Python API client and CLI to manage large numbers of projects, check in your configuration, and more.

Your development team can have an excellent developer experience with a reproducible workspace containing the app code, an anonymized snapshot of production data, and all the tools they need to build, test, and ship.

How we established SSH tunneling for secure, automated Dev & Test environments.

In this tutorial, you will learn how to de-risk sharing data with partners by automatically de-identifying data with Privacy Dynamics.

Improve developer productivity by integrating real data into development and testing environments.

In this tutorial, you will learn how to assess the quality of de-identified data.

The simple and painless way to de-identify senstive data is available in the AWS Marketplace.

In this post, I'll share why teams see value in deploying de-identified datasets alongside masked ones, and how large organizations use both to achieve data minimization without burdensome governance schemes.

Quilt is a lightweight data catalog that improves data governance and reproducibility in data lakes. In this post, we use Quilt to publish a Package of de-identified data with a few lines of Python.

In this article, we’ll review how regulatory revisions under CPRA impact the use of Production data in development and testing environments, the friction it causes between security, compliance, and engineering, and explore an alternative to strict governance policies using de-identified data snapshots.

DuckDB is an incredibly powerful tool enabling analysts and data scientists to move faster and process more data locally. Does this cause a problem for data governance and compliance?

Development teams are using pre-configured environments to increase efficiency and minimize inconsistencies, but this hasn't solved the problem of getting representative and useful data to development teams. Privacy Dynamics, along with modern developer tools like Okteto, can safely bring production data into your development and test environments.

Software development practices have matured dramatically over the last several years. But in spite of all of the other advances, using realistic data for development and testing while complying with privacy laws remains out of reach for most organizations.

Data minimization is an important security, privacy, and compliance strategy, but applying it in an analytics context is hard. In this post, we show you how you can minimize the use of personal information in the Modern Data Stack, using dbt, Snowflake, and Privacy Dynamics.

House Rx can now feel confident that their engineers and analysts have fast and responsible access to the information they need, without having access to information they don’t.

Survival analysis is a powerful tool for understanding the time between any two events, but typically it requires rich data that can re-identify individuals. In this post, we demonstrate how to use anonymized data for survival analysis without degrading the utility of the analysis.

We often get asked about how our solution compares to synthetic data, and are surprised to find that most synthetic data companies aren’t straightforward about the risks and tradeoffs of their approach.

Data classification, also called entity recognition or PII detection, can now be automated with a new feature in Snowflake. In this post, we're going to explore that feature and discuss its design, performance, and limitations.

At Data Council Austin, Will Thompson surveyed the current state-of-the-art privacy methods, and discussed why we think a microaggregation-based approach is the best solution to the privacy-utility tradeoff for most data teams.

Today we published a new dbt package to make it easier to pseudonymize data in your data warehouse.

Data privacy and utility are fundamentally at odds, but with the recent advancements in privacy technology, the question of anonymizing data is shifting from why to why not.

Scalable Strategies For Protecting Data Privacy In Your Shared Data Sets.

Launching a startup is like starting a fire without a match, persistence is paramount.

Privacy-Safe Data Engineering Workflows w/ Graham Thompson.