3 citations · 4 across the 5 of their papers we have counts for
6 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…
Toward provably private analytics and insights into GenAI use
Albert Cheu, Artem Lagzdin, Brett McLarnon +8
Large-scale systems that compute analytics over a fleet of devices must achieve high privacy and security standards while also meeting data quality, usability, and resource efficie…
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…
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…
Confidential Federated Computations
Hubert Eichner, Daniel Ramage, Kallista Bonawitz +11
Federated Learning and Analytics (FLA) have seen widespread adoption by technology platforms for processing sensitive on-device data. However, basic FLA systems have privacy limita…