The workshop focuses on the problem of privacy-preserving machine learning in scenarios where sensitive datasets are distributed across multiple data owners. Such distributed scenarios occur quite often in practice, for example when different parties contribute different records to a dataset, or information about each record in the dataset is held by different owners. Different communities have developed approaches to deal with this problem, including differential privacy-like techniques where noisy sketches are exchanged between the parties, homomorphic encryption where operations are performed on encrypted data, and tailored approaches using techniques from the field of secure multi-party computation. The workshop will serve as a forum to unify different perspectives on this problem and explore the relative merits of each approach. The workshop will also serve as a venue for networking researchers from the machine learning and secure multi-party computation communities interested in private learning, and foster fruitful long-term collaborations.

The workshop will have a particular emphasis in the decentralization aspect of privacy-preserving machine learning. This includes a large number of realistic scenarios where the classical setup of differential privacy with a "trusted curator" that prepares the data cannot be directly applied. The problem of privacy-preserving computation gains relevance in this model, and effectively leveraging the tools developed by the cryptographic community to develop private multi-party learning algorithms poses a remarkable challenge. Our program will include an introductory tutorial to secure multi-party computation for a machine learning audience, and talks by world-renowned experts from the machine learning and cryptography communities who have made high quality contributions to this problem.