Differential privacy has been a de facto privacy standard in defining privacy and handling
privacy preservation. It has had great success in scenarios of local data privacy and …

Large Language Models (LLMs) have emerged as dominant tools for various tasks,
particularly when tailored for a specific target by prompt tuning. Nevertheless, concerns …

Shortly after it was first introduced in 2006, differential privacy became the flagship data
privacy definition. Since then, numerous variants and extensions were proposed to adapt it …

We propose, implement, and evaluate a new algo-rithm for releasing answers to very large
numbersof statistical queries likek-way marginals, sub-ject to differential privacy. Our …

We study the accuracy of differentially private mechanisms in the continual release model. A
continual release mechanism receives a sensitive dataset as a stream of $ T $ inputs and …

We present two sample-efficient differentially private mean estimators for $ d $-dimensional
(sub) Gaussian distributions with unknown covariance. Informally, given $ n\gtrsim d/\alpha …

In-context learning (ICL) is an important capability of Large Language Models (LLMs),
enabling these models to dynamically adapt based on specific, in-context exemplars …

We provide a differentially private algorithm for producing synthetic data simultaneously
useful for multiple tasks: marginal queries and multitask machine learning (ML). A key …

We present a privacy system that leverages differential privacy to protect LinkedIn members'
data while also providing audience engagement insights to enable marketing analytics …

We study the problem of efficiently generating differentially private synthetic data that
approximate the statistical properties of an underlying sensitive dataset. In recent years …