Google announces to open source Differential Privacy

Google has open-sourced its differential privacy platform, which has been used in Google’s internal applications, such as the travel software Project Fi to learn about the busyness of the day, the popularity of the dishes in a particular restaurant on Google Maps.

A differential privacy library is a principled approach that enables organizations to learn from most data while ensuring that these results do not allow for the differentiation or re-identification of any individual’s data. On the one hand is to obtain the value in the data, on the other hand to ensure strong data security. The so-called differentiation, for example, if you are a health researcher, you may want to compare the average time that patients are admitted to different hospitals to determine if there is a difference in care.

Here are some of the key features of the library:

• Statistical functions: Most common data science operations are supported by this release. Developers can compute counts, sums, averages, medians, and percentiles using our library.
• Rigorous testing: Getting differential privacy right is challenging. Besides an extensive test suite, we’ve included an extensible ‘Stochastic Differential Privacy Model Checker library’ to help prevent mistakes.
• Ready to use: The real utility of an open-source release is in answering the question “Can I use this?” That’s why we’ve included a PostgreSQL extension along with common recipes to get you started. We’ve described the details of our approach in a technical paper that we’ve just released today.
• Modular: We designed the library so that it can be extended to include other functionalities such as additional mechanisms, aggregation functions, or privacy budget management.