Privacy-Preserving Machine Learning | Security Research Group

NeurIPS 2018: Distributed Learning without Distress

8 December 2018 privacy-preserving machine learning, secure computation, Bargav Jayaraman

Bargav Jayaraman presented our work on privacy-preserving machine learning at the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018) in Montreal. Distributed learning (sometimes known as federated learning) allows a group of independent data owners to collaboratively learn a model over their data sets without exposing their private data. Our approach combines differential privacy with secure multi-party computation to both protect the data during training and produce a model that provides privacy against inference attacks.