Blog

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, a

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 t

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, is the process of labeling data with its semantic type after inferring the meaning of the data. Data Classification is also the name of a new Snowflake feature, released in public previ

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