2 citations · 3 across the 5 of their papers we have counts for
5 papers
"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…
Models Matter: Setting Accurate Privacy Expectations for Local and Central Differential Privacy
Mary Anne Smart, Priyanka Nanayakkara, Rachel Cummings +2
Differential privacy is a popular privacy-enhancing technology that has been deployed both in industry and government agencies. Unfortunately, existing explanations of differential…
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…
Integrating Differential Privacy and Contextual Integrity
Sebastian Benthall, Rachel Cummings
In this work, we propose the first framework for integrating Differential Privacy (DP) and Contextual Integrity (CI). DP is a property of an algorithm that injects statistical nois…