52 citations · 166 across the 28 of their papers we have counts for
5 papers · 1 filter
Mean Estimation with User-level Privacy under Data Heterogeneity
Rachel Cummings, Vitaly Feldman, Audra McMillan +1
A key challenge in many modern data analysis tasks is that user data are heterogeneous. Different users may possess vastly different numbers of data points. More importantly, it ca…
Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment
Rachel Cummings, Damien Desfontaines, David Evans +21
In this article, we present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP), with a focus of advancing DP's depl…
Differentially Private Synthetic Control
Saeyoung Rho, Rachel Cummings, Vishal Misra
Synthetic control is a causal inference tool used to estimate the treatment effects of an intervention by creating synthetic counterfactual data. This approach combines measurement…
What Are the Chances? Explaining the Epsilon Parameter in Differential Privacy
Priyanka Nanayakkara, Mary Anne Smart, Rachel Cummings +2
Differential privacy (DP) is a mathematical privacy notion increasingly deployed across government and industry. With DP, privacy protections are probabilistic: they are bounded by…
Robust Estimation under the Wasserstein Distance
Sloan Nietert, Rachel Cummings, Ziv Goldfeld
We study the problem of robust distribution estimation under the Wasserstein distance, a popular discrepancy measure between probability distributions rooted in optimal transport (…