Non-parametric Differentially Private Confidence Intervals for the Median. - Jörg Drechsler; Ira Globus-Harris; Audra McMillan; Jayshree Sarathy; Adam D. Smith 001 (CoRR, 2021)

Controlling Privacy Loss in Sampling Schemes: An Analysis of Stratified and Cluster Sampling. - Mark Bun; Jörg Drechsler; Marco Gaboardi; Audra McMillan; Jayshree Sarathy (FORC, 2022)

Differentially Private Simple Linear Regression. - Daniel Alabi; Audra McMillan; Jayshree Sarathy; Adam D. Smith 001; Salil P. Vadhan (Proc. Priv. Enhancing Technol., 2022)

Don't Look at the Data! How Differential Privacy Reconfigures the Practices of Data Science. - Jayshree Sarathy; Sophia Song; Audrey Haque; Tania Schlatter; Salil P. Vadhan (CHI, 2023)

Centering Policy and Practice: Research Gaps Around Usable Differential Privacy. - Rachel Cummings; Jayshree Sarathy (TPS-ISA, 2023)

ATTAXONOMY: Unpacking Differential Privacy Guarantees Against Practical Adversaries. - Rachel Cummings; Shlomi Hod; Jayshree Sarathy; Marika Swanberg (CoRR, 2024)

"I inherently just trust that it works": Investigating Mental Models of Open-Source Libraries for Differential Privacy. - Patrick Song; Jayshree Sarathy; Michael Shoemate; Salil P. Vadhan (Proc. ACM Hum. Comput. Interact., 2024)

Analyzing the Differentially Private Theil-Sen Estimator for Simple Linear Regression. - Jayshree Sarathy; Salil P. Vadhan (Proc. Priv. Enhancing Technol., 2025)