privacy

You are absolutely right, we are leveraging many open-source bricks to build our product, so that they can be reviewed, mainly: - https://github.com/google/differential-privacy (for basic mechanisms and PLD accounting) - https://github.com/tensorflow/privacy (for DP-SGD and RDP accounting) - https://github.com/opendp (for our SQL module) We actively contribute to some of them. We also open-sourced some tech bricks we are using: - https://github.com/sarus-tech/dp-xgboost (see also https://arxiv.org/pdf/2110.12770.pdf) We plan to continue building trust in the tools we are using by publishing some of them.

“TensorFlow Federated (TFF) is an open-source framework for machine learning and other computations on decentralized data. TFF has been developed to facilitate open research and experimentation with Federated Learning (FL), an approach to machine learning where a shared global model is trained across many participating clients that keep their training data locally. For example, FL has been used to train prediction models for mobile keyboards without uploading sensitive typing data to servers.“ From https://www.tensorflow.org/federated This is a very exiciting project that compliments other privacy-preserving machine learning techniques (PPML) in TensorFlow: - Differential Privacy: https://github.com/tensorflow/privacy - Secure Multi-Party Computation: https://github.com/mortendahl/tf-encrypted - Confidential Computing (Trusted Execution E