Designing a Distributed, Privacy-Preserving Analytics Architecture with Personal Clouds
Ryan Tatton (Department of Computer and Data Sciences)
Freedman Student Fellow 2021–2022
Ryan Tatton is a Master’s candidate in the Department of Computer and Data Sciences with a focus in artificial intelligence. His grant research involves developing a privacy-preserving, distributed, cloud-based architecture that can perform analytics on user data transparently and at scale.
This project aims to address the privacy concerns of increasingly personalized modern applications that derive insight from user data. Specifically, he aims to develop a general distributed message-passing solution that allows for implementations of specific federated learning and otherwise distributed algorithms. Once the architecture is implemented, the privacy guarantees, scalability, and efficiency will be studied with various algorithms and threat models.
