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Found 10 papers, 5 papers with code
Membership Inference Attacks and Privacy in Topic Modeling
Recent research shows that large language models are susceptible to privacy attacks that infer aspects of the training data.
Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training
The surge in multimodal AI's success has sparked concerns over data privacy in vision-and-language tasks.
DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian Inference
In this paper, we study Metropolis-Hastings (MH), one of the most fundamental MCMC methods, for large-scale Bayesian inference under differential privacy.
Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size
We also empirically validate our theoretical results on several synthetic databases, showing that our algorithms also perform well in practice.
Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control
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 control constraint in the sense that one might be able to only observe or use selected components data for decision-making per time step in the resource-constrained environments.
Attribute Privacy: Framework and Mechanisms
We propose definitions to capture \emph{attribute privacy} in two relevant cases where global attributes may need to be protected: (1) properties of a specific dataset and (2) parameters of the underlying distribution from which dataset is sampled.
Leakage of Dataset Properties in Multi-Party Machine Learning
Using multiple machine learning models, we show that leakage occurs even if the sensitive attribute is not included in the training data and has a low correlation with other attributes or the target variable.
PAPRIKA: Private Online False Discovery Rate Control
In this work, we study False Discovery Rate (FDR) control in multiple hypothesis testing under the constraint of differential privacy for the sample.
Privately detecting changes in unknown distributions
Much of the prior work on change-point detection---including the only private algorithms for this problem---requires complete knowledge of the pre-change and post-change distributions.
Differentially Private Change-Point Detection
The change-point detection problem seeks to identify distributional changes at an unknown change-point k* in a stream of data.
