Edo Roth

Hello and welcome! I am a privacy researcher and engineer, and I currently work as a software engineer on a federated analytics research team at Google. I received my PhD from the Department of Computer and Information Science at the University of Pennsylvania in 2022. I was fortunate to be advised by Andreas Haeberlen, and I also spent two great summers interning at Microsoft Research.

My past research has focused on privacy and security in data analytics systems, particularly in large-scale distributed ones. I have worked on differential privacy, applied cryptography, federated learning, and distributed systems.

In general, I'm motivated to work on public-interest technology and studying/helping to build trustworthy and healthy digital infrastructure. I'm particularly curious about whether (and how) more human-respecting digital infrastructure can help enhance democracy and decrease inequality.

I grew up in Champaign-Urbana, Illinois, and completed my bachelor studies in Computer Science at Columbia University.

Research

2024

Releasing Large-Scale Human Mobility Histograms with Differential Privacy

Christopher Bian, Albert Cheu, Yannis Guzman, Marco Gruteser, Peter Kairouz, Ryan McKenna, Edo Roth*.
*authors in alphabetical order, was one of two primary contributors.
[PDF]

2023

Arboretum: A Planner for Massive-Scale Federated Analytics with Differential Privacy

Elizabeth Margolin, Karan Newatia, Edo Roth, Tao Luo, and Andreas Haeberlen.
SOSP 2023, Koblenz, Germany
[PDF]

2021

Mycelium: Large-Scale Distributed Graph Queries with Differential Privacy

Edo Roth, Karan Newatia, Yiping Ma, Ke Zhong, Sebastian Angel, Andreas Haeberlen.
SOSP 2021, Virtual.
[PDF] [Talk] [Slides]

Do Not Overpay for Fault Tolerance!

Edo Roth, Andreas Haeberlen.
RTAS 2021, Nashville, TN, USA (Virtual).
[PDF] [Talk] [Slides]

REBOUND: Defending Distributed Systems Against Attacks with Bounded-Time Recovery

Neeraj Gandhi, Edo Roth,, Brian Sandler, Andreas Haeberlen, Linh Thi Xuan Phan.
EuroSys 2021, Edinburgh, UK (Virtual).
[PDF] [Talk] [Slides]

2020

Orchard: Differentially Private Analytics at Scale

Edo Roth, Hengchu Zhang, Andreas Haeberlen, Benjamin Pierce.
OSDI 2020, Banff, Canada (Virtual).
[PDF] [Talk] [Slides]

Testing Differential Privacy with Dual Interpreters

Hengchu Zhang, Edo Roth, Benjamin Pierce, Aaron Roth, Andreas Haeberlen.
OOPSLA 2020, Chicago, IL, USA.
[PDF]

Bounded-Time Recovery for Distributed Real-Time Systems

Neeraj Gandhi, Edo Roth, Robert Gifford, Linh Thi Xuan Phan, Andreas Haeberlen.
RTAS 2020, Sydney, Australia.
[PDF]

2019

Honeycrisp: Large-scale Differentially Private Aggregation Without a Trusted Core

Edo Roth, Daniel Noble, Andreas Haeberlen, Brett Hemenway.
SOSP 2019, Huntsville, Ontario, Canada.
[PDF] [Slides] [Talk]

Fuzzi: A Three-Level Logic for Differential Privacy

Hengchu Zhang, Edo Roth, Andreas Haeberlen, Benjamin C Pierce, Aaron Roth.
ICFP 2019, Berlin, Germany.
[PDF]

Edo Roth edoroth@duck.com