4 citations · 6 across the 8 of their papers we have counts for
10 papers
SHIP: Structured and Hierarchical Insight with Privacy
Kai Chen, Guisi Ni, Albert Cheu +4
Extracting insights from textual data while protecting user privacy is increasingly important. Existing methods produce private insights in diverse forms for different analytical p…
JAX-Privacy: A library for differentially private machine learning
Ryan McKenna, Galen Andrew, Borja Balle +6
JAX-Privacy is a library designed to simplify the deployment of robust and performant mechanisms for differentially private machine learning. Guided by design principles of usabili…
Privately Fine-Tuned LLMs Preserve Temporal Dynamics in Tabular Data
Lucas Rosenblatt, Peihan Liu, Ryan McKenna +1
Research on differentially private synthetic tabular data has largely focused on independent and identically distributed rows where each record corresponds to a unique individual.…
How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy
Natalia Ponomareva, Zheng Xu, H. Brendan McMahan +12
High quality data is needed to unlock the full potential of AI for end users. However finding new sources of such data is getting harder: most publicly-available human generated da…
ACTG-ARL: Differentially Private Conditional Text Generation with RL-Boosted Control
Yuzheng Hu, Ryan McKenna, Da Yu +4
Generating high-quality synthetic text under differential privacy (DP) is critical for training and evaluating language models without compromising user privacy. Prior work on synt…
Benchmarking Differentially Private Tabular Data Synthesis
Kai Chen, Xiaochen Li, Chen Gong +2
Differentially private (DP) tabular data synthesis generates artificial data that preserves the statistical properties of private data while safeguarding individual privacy. The em…
Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data?
Marika Swanberg, Ryan McKenna, Edo Roth +2
Differentially private (DP) synthetic data is a versatile tool for enabling the analysis of private data. Recent advancements in large language models (LLMs) have inspired a number…
Learn from the Past: Language-conditioned Object Rearrangement with Large Language Models
Guanqun Cao, Ryan Mckenna, Erich Graf +1
Object manipulation for rearrangement into a specific goal state is a significant task for collaborative robots. Accurately determining object placement is a key challenge, as misa…
Mayfly: Private Aggregate Insights from Ephemeral Streams of On-Device User Data
Christopher Bian, Albert Cheu, Stanislav Chiknavaryan +12
This paper introduces Mayfly, a federated analytics approach enabling aggregate queries over ephemeral on-device data streams without central persistence of sensitive user data. Ma…
Releasing Large-Scale Human Mobility Histograms with Differential Privacy
Christopher Bian, Albert Cheu, Yannis Guzman +4
Environmental Insights Explorer (EIE) is a Google product that reports aggregate statistics about human mobility, including various methods of transit used by people across roughly…