35 citations · 70 across the 10 of their papers we have counts for
11 papers
Differential Perspectives: Epistemic Disconnects Surrounding the US Census Bureau's Use of Differential Privacy
danah boyd, Jayshree Sarathy
When the U.S. Census Bureau announced its intention to modernize its disclosure avoidance procedures for the 2020 Census, it sparked a controversy that is still underway. The move…
Statistical Imaginaries, State Legitimacy: Grappling with the Arrangements Underpinning Quantification in the U.S. Census
Jayshree Sarathy, danah boyd
Over the last century, the adoption of novel scientific methods for conducting the U.S. census has been met with wide-ranging receptions. Some methods were quietly embraced, while…
"Having Confidence in My Confidence Intervals": How Data Users Engage with Privacy-Protected Wikipedia Data
Harold Triedman, Jayshree Sarathy, Priyanka Nanayakkara +4
In response to calls for open data and growing privacy threats, organizations are increasingly adopting privacy-preserving techniques such as differential privacy (DP) that inject…
"I inherently just trust that it works": Investigating Mental Models of Open-Source Libraries for Differential Privacy
Patrick Song, Jayshree Sarathy, Michael Shoemate +1
Differential privacy (DP) is a promising framework for privacy-preserving data science, but recent studies have exposed challenges in bringing this theoretical framework for privac…
Centering Policy and Practice: Research Gaps around Usable Differential Privacy
Rachel Cummings, Jayshree Sarathy
As a mathematically rigorous framework that has amassed a rich theoretical literature, differential privacy is considered by many experts to be the gold standard for privacy-preser…
ATTAXONOMY: Unpacking Differential Privacy Guarantees Against Practical Adversaries
Rachel Cummings, Shlomi Hod, Jayshree Sarathy +1
Differential Privacy (DP) is a mathematical framework that is increasingly deployed to mitigate privacy risks associated with machine learning and statistical analyses. Despite the…
Don't Look at the Data! How Differential Privacy Reconfigures the Practices of Data Science
Jayshree Sarathy, Sophia Song, Audrey Haque +2
Across academia, government, and industry, data stewards are facing increasing pressure to make datasets more openly accessible for researchers while also protecting the privacy of…
Analyzing the Differentially Private Theil-Sen Estimator for Simple Linear Regression
Jayshree Sarathy, Salil Vadhan
In this paper, we study differentially private point and confidence interval estimators for simple linear regression. Motivated by recent work that highlights the strong empirical…
Non-parametric Differentially Private Confidence Intervals for the Median
Joerg Drechsler, Ira Globus-Harris, Audra McMillan +2
Differential privacy is a restriction on data processing algorithms that provides strong confidentiality guarantees for individual records in the data. However, research on proper…
Differentially Private Simple Linear Regression
Daniel Alabi, Audra McMillan, Jayshree Sarathy +2
Economics and social science research often require analyzing datasets of sensitive personal information at fine granularity, with models fit to small subsets of the data. Unfortun…
Controlling Privacy Loss in Sampling Schemes: an Analysis of Stratified and Cluster Sampling
Mark Bun, Jörg Drechsler, Marco Gaboardi +2
Sampling schemes are fundamental tools in statistics, survey design, and algorithm design. A fundamental result in differential privacy is that a differentially private mechanism r…