Interview with Chinmay Shah: Differential Privacy Team Lead at OpenMined

In this interview, Chinmay Shah, who leads the Differential Privacy team at OpenMined, talks about his interests, the team's journey and the exciting road ahead!

Private AI: Machine Learning on Encrypted Data

Protect privacy of your data by encrypting it. Outsource computations on the encrypted data, and decrypt at your end to view results.

Adaptive Federated Optimization

In non-federated settings, adaptive optimization methods have desirable convergence properties. Can federated versions of these adaptive optimizers, including Adagrad, Adam, and Yogi facilitate better convergence in the presence of heterogeneous data?

Differential Privacy using PyDP

Differential Privacy using PyDP - An introductory tutorial. Here's an outline: What does Differential Privacy try to address? Why doesn't anonymization suffice? PyDP Example Walkthrough

Advances and Open Problems in Federated Learning

What are some of the recent advances in Federated Learning? What challenges do the privacy principles guiding Federated Learning (FL) bring into the system?

What's in the TensorFlow Federated (TFF) box?

TensorFlow Federated (TFF) is a new development framework for Federated Computations (FC). Here's a summary of TFF's design goals and capabilities.

Tempered Sigmoid Activations for Deep Learning with Differential Privacy

Read to find out how tempered sigmoid activations help overcome the problem of exploding gradients and yield better accuracy under differentially private model training.

PrivacyRaven: Comprehensive Privacy Testing for Deep Learning

Access to only the output labels is a seemingly restrictive setting. What is an adversary modeled by PrivacyRaven capable of, given this restrictive setting?