28 citations · 91 across the 12 of their papers we have counts for
12 papers
Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training
Alyssa Huang, Peihan Liu, Ryumei Nakada +2
The surge in multimodal AI's success has sparked concerns over data privacy in vision-and-language tasks. While CLIP has revolutionized multimodal learning through joint training o…
Continual Release of Differentially Private Synthetic Data from Longitudinal Data Collections
Mark Bun, Marco Gaboardi, Marcel Neunhoeffer +1
Motivated by privacy concerns in long-term longitudinal studies in medical and social science research, we study the problem of continually releasing differentially private synthet…
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…
DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian Inference
Wanrong Zhang, Ruqi Zhang
Bayesian inference provides a principled framework for learning from complex data and reasoning under uncertainty. It has been widely applied in machine learning tasks such as medi…
Concurrent Composition Theorems for Differential Privacy
Salil Vadhan, Wanrong Zhang
We study the concurrent composition properties of interactive differentially private mechanisms, whereby an adversary can arbitrarily interleave its queries to the different mechan…
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…
Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control
Wanrong Zhang, Yajun Mei
In many real-world problems of real-time monitoring high-dimensional streaming data, one wants to detect an undesired event or change quickly once it occurs, but under the sampling…
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…
Leakage of Dataset Properties in Multi-Party Machine Learning
Wanrong Zhang, Shruti Tople, Olga Ohrimenko
Secure multi-party machine learning allows several parties to build a model on their pooled data to increase utility while not explicitly sharing data with each other. We show that…
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…
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…