52 citations · 166 across the 28 of their papers we have counts for
30 papers
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 (…
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…
Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis
Sloan Nietert, Rachel Cummings, Ziv Goldfeld
The Wasserstein distance, rooted in optimal transport (OT) theory, is a popular discrepancy measure between probability distributions with various applications to statistics and ma…
"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…
Attribute Privacy: Framework and Mechanisms
Wanrong Zhang, Olga Ohrimenko, Rachel Cummings
Ensuring the privacy of training data is a growing concern since many machine learning models are trained on confidential and potentially sensitive data. Much attention has been de…
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…
PAPRIKA: Private Online False Discovery Rate Control
Wanrong Zhang, Gautam Kamath, Rachel Cummings
In hypothesis testing, a false discovery occurs when a hypothesis is incorrectly rejected due to noise in the sample. When adaptively testing multiple hypotheses, the probability o…
Algorithmic Price Discrimination
Rachel Cummings, Nikhil R. Devanur, Zhiyi Huang +1
We consider a generalization of the third degree price discrimination problem studied in Bergemann et al. (2015), where an intermediary between the buyer and the seller can design…
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent +56
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…
Differentially Private Synthetic Mixed-Type Data Generation For Unsupervised Learning
Uthaipon Tantipongpipat, Chris Waites, Digvijay Boob +2
We introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of Generative Adversari…
Privately detecting changes in unknown distributions
Rachel Cummings, Sara Krehbiel, Yuliia Lut +1
The change-point detection problem seeks to identify distributional changes in streams of data. Increasingly, tools for change-point detection are applied in settings where data ma…
Differentially Private Change-Point Detection
Rachel Cummings, Sara Krehbiel, Yajun Mei +2
The change-point detection problem seeks to identify distributional changes at an unknown change-point k* in a stream of data. This problem appears in many important practical sett…
Differentially Private Online Submodular Optimization
Adrian Rivera Cardoso, Rachel Cummings
In this paper we develop the first algorithms for online submodular minimization that preserve differential privacy under full information feedback and bandit feedback. A sequence…
Individual Sensitivity Preprocessing for Data Privacy
Rachel Cummings, David Durfee
The sensitivity metric in differential privacy, which is informally defined as the largest marginal change in output between neighboring databases, is of substantial significance i…
Differential Privacy for Growing Databases
Rachel Cummings, Sara Krehbiel, Kevin A. Lai +1
We study the design of differentially private algorithms for adaptive analysis of dynamically growing databases, where a database accumulates new data entries while the analysis is…
The Possibilities and Limitations of Private Prediction Markets
Rachel Cummings, David M. Pennock, Jennifer Wortman Vaughan
We consider the design of private prediction markets, financial markets designed to elicit predictions about uncertain events without revealing too much information about market pa…
Adaptive Learning with Robust Generalization Guarantees
Rachel Cummings, Katrina Ligett, Kobbi Nissim +2
The traditional notion of generalization---i.e., learning a hypothesis whose empirical error is close to its true error---is surprisingly brittle. As has recently been noted in [DF…