This page was created by Manish Tyagi.
Designing a Distributed, Privacy-Preserving Analytics Architecture with Personal Clouds
Participant - Ryan Tatton
Mentions
- https://scalar.case.edu/freedman-fellows/tatton-2021-2022
- https://thedaily.case.edu/meet-the-winners-of-the-2021-2022-walter-freedman-and-karen-harrison-freedman-student-fellowships/
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.