DEFCON Differential Privacy Training Launch

Differential privacy is a technique that enables organizations to learn from the majority of their data while simultaneously ensuring those results do not allow an individual’s data to be distinguished or re-identified. A popular way of attaining differential privacy is by adding noise to the data, which provides mathematical bounds on the amount of information that is leaked. Our open source offering aims to help developers implement differential privacy.

In the summer of 2019, we publicly launched our Differential Privacy Library. Since then, we’ve expanded it from just C++ to also include Go and Java.

We’ve come to realize that differential privacy requires more than just the library to be effectively implemented. We mentioned in a post earlier this summer that we want all developers to be able to interact with differential privacy, which requires more than an open-sourced library, but rather a training on the topic to share knowledge with all developers.

Our goal with this training is to provide a head start that is helpful for those considering differential privacy implementation. We also want to provide an experience on privacy and security that is understood and impactful to any individual in the field, whether they are a beginner or someone who has background knowledge in privacy.

This new training contains several steps and covers many topics, such as:
  • The foundations of differential privacy
  • Explanations as to why aggregation by itself may not hedge against privacy risks
  • The mathematical behind-the-scenes of noise
  • Tools that can be used in conjunction with differential privacy
  • Codelabs that users can take (in Go)
  • Additional resources to address any further questions

Step 1: Take our survey! It only takes five minutes!

This survey enables us to gain insights into what you are expecting to gain from this training. We are curious about what your objectives and goals are with this training, and if you have any experience with differential privacy.

Step 2: Check-out an introductory video to Differential Privacy!

We introduce topics like data aggregation, k-anonymity, differential privacy, noise, and others. The goal of this module is to introduce the foundations behind the differential privacy, and why it is an important and useful privacy tool.

Step 3: Try-out our codelabs

We have provided Codelabs in Go to help you practice implementing Differential Privacy library end-to-end.

Step 4: Learn more about differential privacy.

We want to offer an additional resource to help answer any questions you may have. If you have other resources that you find, please let us know and we will add these links to our overall training.

Step 5: Provide us with some feedback

Please use this survey as a platform to share your experience with this pilot. Did the content meet your expectations? Did it make sense? What was missing? This is the time for you to share your point of view and any pain points you experienced (as well as any positive aspects you encountered).

We hope this training provides an impactful experience from beginner coders to privacy specialists. The public differential privacy training will launch at the Stanford Biodesign: “Building for Digital Health” Buildathon, Sept 11-13, 2020, led by Stanford, and supported by Google Cloud and Apple Health engineers.

Please continue to reach out to us to share your experiences with us at [email protected]. The suggestions we receive will help us improve and it will inform our thinking as we add new features and updates.

Acknowledgements: Miguel Guevara, Bryant Gipson, Royce Wilson, Kate Frankenberg, Katie Holzheimer, Lior Gottleib, Carmen Bush

By Aditi Joshi – Security and Privacy Engineering, Google Cloud