4 citations · 11 across the 7 of their papers we have counts for
8 papers
Toward provably private learning from federated data
Katharine Daly, Yu Xiao, Zachary Garrett +9
Federated Learning (FL) allows devices with private data to collaborate in training a shared model. We present a next-generation FL system based on Trusted Execution Environments (…
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…
Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications
Yanxiang Zhang, Zheng Xu, Shanshan Wu +2
Error correction is an important capability when applying large language models (LLMs) to facilitate user typing on mobile devices. In this paper, we use LLMs to synthesize a high-…
Trusted Machine Learning Models Unlock Private Inference for Problems Currently Infeasible with Cryptography
Ilia Shumailov, Daniel Ramage, Sarah Meiklejohn +4
We often interact with untrusted parties. Prioritization of privacy can limit the effectiveness of these interactions, as achieving certain goals necessitates sharing private data.…
Federated Learning in Practice: Reflections and Projections
Katharine Daly, Hubert Eichner, Peter Kairouz +3
Federated Learning (FL) is a machine learning technique that enables multiple entities to collaboratively learn a shared model without exchanging their local data. Over the past de…
AirGapAgent: Protecting Privacy-Conscious Conversational Agents
Eugene Bagdasarian, Ren Yi, Sahra Ghalebikesabi +5
The growing use of large language model (LLM)-based conversational agents to manage sensitive user data raises significant privacy concerns. While these agents excel at understandi…
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…
Prompt Public Large Language Models to Synthesize Data for Private On-device Applications
Shanshan Wu, Zheng Xu, Yanxiang Zhang +2
Pre-training on public data is an effective method to improve the performance for federated learning (FL) with differential privacy (DP). This paper investigates how large language…