3 papers
cs.HC2024
"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…
cs.CR2024
Private Means and the Curious Incident of the Free Lunch
Jack Fitzsimons, James Honaker, Michael Shoemate +1
We show that the most well-known and fundamental building blocks of DP implementations -- sum, mean, count (and many other linear queries) -- can be released with substantially red…
cs.CR2023
Concurrent Composition for Interactive Differential Privacy with Adaptive Privacy-Loss Parameters
Samuel Haney, Michael Shoemate, Grace Tian +4
In this paper, we study the concurrent composition of interactive mechanisms with adaptively chosen privacy-loss parameters. In this setting, the adversary can interleave queries t…