Synthetic Data as an Enabler for Learning from Decentralized Private Data

Today, data sharing is the cornerstone of many modern applications. A common concern in such data-sharing pipelines is privacy: organizations are responsible for protecting the privacy of their data, whether it represents user data or enterprise trade secrets. In her talk from the recent workshop on Trust in Decentralized Systems, Giulia Fanti (Carnegie Mellon) discussed emerging challenges related to learning from private, federated data.