52 citations · 83 across the 7 of their papers we have counts for
17 papers
Optimal Data Acquisition with Privacy-Aware Agents
Rachel Cummings, Hadi Elzayn, Vasilis Gkatzelis +2
We study the problem faced by a data analyst or platform that wishes to collect private data from privacy-aware agents. To incentivize participation, in exchange for this data, the…
Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size
Wanrong Zhang, Yajun Mei, Rachel Cummings
The sequential hypothesis testing problem is a class of statistical analyses where the sample size is not fixed in advance. Instead, the decision-process takes in new observations…
"I need a better description'': An Investigation Into User Expectations For Differential Privacy
Rachel Cummings, Gabriel Kaptchuk, Elissa M. Redmiles
Despite recent widespread deployment of differential privacy, relatively little is known about what users think of differential privacy. In this work, we seek to explore users' pri…
How we browse: Measurement and analysis of digital behavior
Yuliia Lut, Michael Wang, Elissa M. Redmiles +1
Accurately analyzing and modeling online browsing behavior play a key role in understanding users and technology interactions. In this work, we design and conduct a user study to c…
Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation
Chris Waites, Rachel Cummings
Normalizing flow models have risen as a popular solution to the problem of density estimation, enabling high-quality synthetic data generation as well as exact probability density…
Optimal Local Explainer Aggregation for Interpretable Prediction
Qiaomei Li, Rachel Cummings, Yonatan Mintz
A key challenge for decision makers when incorporating black box machine learned models into practice is being able to understand the predictions provided by these models. One prop…